{"id":20285,"date":"2025-03-06T05:39:22","date_gmt":"2025-03-06T05:39:22","guid":{"rendered":"https:\/\/www.pickl.ai\/blog\/?p=20285"},"modified":"2025-03-06T05:39:23","modified_gmt":"2025-03-06T05:39:23","slug":"accuracy-machine-learning-model","status":"publish","type":"post","link":"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/","title":{"rendered":"How Can You Check the Accuracy of Your Machine Learning Model?"},"content":{"rendered":"\n<p>Summary: Accuracy in Machine Learning measures correct predictions but can be deceptive, particularly with imbalanced or multilabel data. The blog explains the limitations of using accuracy alone. It introduces alternative metrics like precision, recall, F1-score, confusion matrices, ROC curves, and Hamming metrics to evaluate models, ensuring improved insights comprehensively.<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Accuracy_in_Machine_Learning\" >Accuracy in Machine Learning<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Using_Accuracy_Score_in_Python\" >Using Accuracy Score in Python<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#How_Accuracy_Works_in_Binary_Classification\" >How Accuracy Works in Binary Classification<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#The_Accuracy_Paradox\" >The Accuracy Paradox<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#How_Class_Imbalance_Affects_Accuracy\" >How Class Imbalance Affects Accuracy<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Example\" >Example<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Why_High_Accuracy_Can_Be_Deceptive\" >Why High Accuracy Can Be Deceptive<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Alternatives_to_Accuracy_for_Model_Evaluation\" >Alternatives to Accuracy for Model Evaluation<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#When_is_Accuracy_Not_Reliable\" >When is Accuracy Not Reliable?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Precision_When_False_Positives_Matter\" >Precision: When False Positives Matter<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Recall_Sensitivity_When_Missing_Positives_is_Costly\" >Recall (Sensitivity): When Missing Positives is Costly<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#F1_Score_Balancing_Precision_and_Recall\" >F1 Score: Balancing Precision and Recall<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Confusion_Matrix_Understanding_Errors\" >Confusion Matrix: Understanding Errors<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#ROC_Curve_AUC_Measuring_Trade-offs\" >ROC Curve &amp; AUC: Measuring Trade-offs<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#PR-Curve_Handling_Imbalanced_Data\" >PR-Curve: Handling Imbalanced Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Matthews_Correlation_Coefficient_A_Balanced_Metric\" >Matthews Correlation Coefficient: A Balanced Metric<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Accuracy_in_Multiclass_Problems\" >Accuracy in Multiclass Problems<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#What_is_Accuracy_in_Multiclass_Classification\" >What is Accuracy in Multiclass Classification?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Example_Confusion_Matrix_for_a_3-Class_Problem\" >Example: Confusion Matrix for a 3-Class Problem<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Case_Study_Predicting_the_Iris_Dataset_with_a_Decision_Tree\" >Case Study: Predicting the Iris Dataset with a Decision Tree<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Why_Class-Level_Recall_is_More_Informative\" >Why Class-Level Recall is More Informative<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Accuracy_in_Multilabel_Problems\" >Accuracy in Multilabel Problems<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Challenges_in_Multilabel_Settings\" >Challenges in Multilabel Settings<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Example_Using_the_RCV1_Dataset\" >Example Using the RCV1 Dataset<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Why_Standard_Accuracy_is_Misleading_in_Multilabel_Problems\" >Why Standard Accuracy is Misleading in Multilabel Problems<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Subset_Accuracy_Exact_Match_Ratio_in_Multi-Label_Classification\" >Subset Accuracy (Exact Match Ratio) in Multi-Label Classification<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#What_is_Subset_Accuracy\" >What is Subset Accuracy?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Challenges_with_Subset_Accuracy\" >Challenges with Subset Accuracy<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Better_Alternatives_for_Evaluating_Multi-Label_Models\" >Better Alternatives for Evaluating Multi-Label Models<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Hamming_Score\" >Hamming Score<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Hamming_Loss\" >Hamming Loss<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-33\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Precision_Recall_and_F1_Score\" >Precision, Recall, and F1 Score<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-34\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Additional_Accuracy_Metrics\" >Additional Accuracy Metrics<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-35\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Balanced_Accuracy_Handling_Imbalanced_Datasets\" >Balanced Accuracy: Handling Imbalanced Datasets<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-36\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Top-K_Accuracy_Useful_for_Recommendations_and_Image_Recognition\" >Top-K Accuracy: Useful for Recommendations and Image Recognition<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-37\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Accuracy_of_Probability_Predictions_Measuring_Confidence_in_Predictions\" >Accuracy of Probability Predictions: Measuring Confidence in Predictions<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-38\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#In_The_End\" >In The End<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-39\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-40\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#What_is_Accuracy_in_Machine_Learning_and_Why_is_it_Important\" >What is Accuracy in Machine Learning, and Why is it Important?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-41\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#Why_can_Accuracy_in_Machine_Learning_be_Misleading_in_Certain_Scenarios\" >Why can Accuracy in Machine Learning be Misleading in Certain Scenarios?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-42\" href=\"https:\/\/www.pickl.ai\/blog\/accuracy-machine-learning-model\/#What_Alternative_Metrics_Should_I_Use_Instead_of_Relying_Solely_on_Accuracy_in_Machine_Learning\" >What Alternative Metrics Should I Use Instead of Relying Solely on Accuracy in Machine Learning?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 id=\"introduction\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><strong>Introduction<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>When you work with <a href=\"https:\/\/pickl.ai\/blog\/what-is-machine-learning\/\">Machine Learning<\/a>, accuracy is the easiest way to measure success. It tells you how often a model makes correct predictions, making it a popular choice. However, relying only on accuracy in Machine Learning can be misleading, especially in real-world situations. What if your data is unbalanced or errors have serious consequences?&nbsp;<\/p>\n\n\n\n<p>In this blog, you\u2019ll learn why accuracy isn\u2019t always the best metric, its challenges, and when to use alternative metrics. By the end, you\u2019ll understand how to evaluate <a href=\"https:\/\/pickl.ai\/blog\/machine-learning-models\/\">Machine Learning models<\/a> effectively, even without technical expertise.<\/p>\n\n\n\n<p><strong>Key Takeaways:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Accuracy in Machine Learning is a widely used metric.<\/li>\n\n\n\n<li>Relying solely on accuracy can mislead evaluations.<\/li>\n\n\n\n<li>Imbalanced and multilabel data require alternative metrics.<\/li>\n\n\n\n<li>Precision, recall, and F1-score reveal deeper performance insights.<\/li>\n\n\n\n<li>Comprehensive evaluation improves model reliability and outcomes.<\/li>\n<\/ul>\n\n\n\n<h2 id=\"accuracy-in-machine-learning\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Accuracy_in_Machine_Learning\"><\/span><strong>Accuracy in Machine Learning<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Accuracy is a way to measure how well a Machine Learning model makes predictions. It tells us the percentage of correct predictions out of the total predictions made. A high accuracy means the model performs well, while a low accuracy indicates improvement.<\/p>\n\n\n\n<p>The formula for accuracy is:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdLc-_awuhDaftHEjLBzithM9gkiyZxpLriAEBvZO9PuOgUtF5E8SFGpBiOW0YzKkt03jrIVbLaaBZ9b1d7FlF8u13lYTnlAmThOOBpZdryY3gan5n-2SBkmfbr7bGo1s9pD7eaxQ?key=m2HxfaXVHcLRwwa34XSqEC7I\" alt=\"Formula for calculating the accuracy.\u00a0\"\/><\/figure>\n\n\n\n<p>For example, if a model makes 100 predictions and 90 are correct, the accuracy is 90%.<\/p>\n\n\n\n<h3 id=\"using-accuracy-score-in-python\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Using_Accuracy_Score_in_Python\"><\/span><strong>Using Accuracy Score in Python<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>In Python, we can calculate accuracy using the accuracy_score function from the sklearn.metrics module. Here&#8217;s a simple example:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcFJxpEUFwxsXA9RFj9pDEP_QzY35LJxmStGCUKM2crwgLfZNKWL2z9400zlSUlX0pwPxO-nz1EMWYspIUfD0p_UU_j9fwowKkfzrNibhXPRowj5CR6f7HUraths4dOXhNJpr5lBg?key=m2HxfaXVHcLRwwa34XSqEC7I\" alt=\" Python code to calculate accuracy using sklearn.\"\/><\/figure>\n\n\n\n<h3 id=\"how-accuracy-works-in-binary-classification\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_Accuracy_Works_in_Binary_Classification\"><\/span><strong>How Accuracy Works in Binary Classification<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>In <a href=\"https:\/\/pickl.ai\/blog\/classification-algorithm-in-machine-learning\/\">binary classification<\/a>, the model predicts one of two possible outcomes (e.g., &#8220;Yes&#8221; or &#8220;No&#8221;). Accuracy measures how often the model gets it right. However, accuracy alone may be insufficient if the data is imbalanced (one class appears much more often than the other). In such cases, additional metrics like precision and recall help evaluate performance better.<\/p>\n\n\n\n<h2 id=\"the-accuracy-paradox\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Accuracy_Paradox\"><\/span><strong>The Accuracy Paradox<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The accuracy paradox happens when a model appears highly accurate but performs poorly in real-world situations. This occurs because accuracy, as a measure, does not always tell the full story of a model\u2019s performance.<\/p>\n\n\n\n<h3 id=\"how-class-imbalance-affects-accuracy\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_Class_Imbalance_Affects_Accuracy\"><\/span><strong>How Class Imbalance Affects Accuracy<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Class imbalance means that one outcome occurs much more often than another. For example, in a cancer prediction model, most cases may be non-cancerous, while only a few cases are cancerous. If the model predicts &#8220;no cancer&#8221; for everyone, it may still show 98% accuracy. However, it completely fails to detect cancer in those who have it.<\/p>\n\n\n\n<h3 id=\"example\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Example\"><\/span><strong>Example<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Let\u2019s consider a model trained to detect breast cancer using the Wisconsin Breast Cancer Dataset. Of 100 cases, 95 are benign (non-cancerous) and only 5 are malignant (cancerous). If the model predicts all cases as benign, it would be 95% accurate\u2014but it would miss all cancer cases, which is dangerous.<\/p>\n\n\n\n<h3 id=\"why-high-accuracy-can-be-deceptive\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Why_High_Accuracy_Can_Be_Deceptive\"><\/span><strong>Why High Accuracy Can Be Deceptive<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>In imbalanced datasets like cancer detection, accuracy alone is not a good measure. Other metrics like precision, recall, and F1-score help determine how well a model identifies actual cancer cases. Always look beyond accuracy to ensure a model makes meaningful predictions.<\/p>\n\n\n\n<h2 id=\"alternatives-to-accuracy-for-model-evaluation\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Alternatives_to_Accuracy_for_Model_Evaluation\"><\/span><strong>Alternatives to Accuracy for Model Evaluation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcD-xStvZocLiMhlne9VCmOF9d3qS6Vqi7ysrCRSFoCdCTpOG48CkkCDL-r7U-bJKYeMvZccTHNO2-FF6e_BgW41ZhDKrBX4dwJaVxeEP1sgY9ia9cCq0BHkToP9CqCuKTHzlVI?key=m2HxfaXVHcLRwwa34XSqEC7I\" alt=\"Alternatives to accuracy for model evaluation.\"\/><\/figure>\n\n\n\n<p>Accuracy is one of the most commonly used metrics to evaluate Machine Learning models. However, it is not always the best choice, especially when dealing with imbalanced datasets or situations where false positives or negatives carry significant consequences. Let&#8217;s explore when accuracy is not reliable and the better alternatives available.<\/p>\n\n\n\n<h3 id=\"when-is-accuracy-not-reliable\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"When_is_Accuracy_Not_Reliable\"><\/span><strong>When is Accuracy Not Reliable?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Accuracy measures the percentage of correctly predicted outcomes but does not consider the type of errors made by a model. For example, in medical diagnosis, a model that predicts &#8220;no disease&#8221; for everyone may still have high accuracy if most people are healthy. However, it fails to detect actual patients, which can have serious consequences.<\/p>\n\n\n\n<p>Before relying on accuracy, ask yourself:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Is the dataset imbalanced (more of one class than the other)?<\/li>\n\n\n\n<li>Are false positives or false negatives more critical in this case?<\/li>\n\n\n\n<li>Does accuracy truly reflect the model&#8217;s usefulness?<\/li>\n<\/ul>\n\n\n\n<p>If the answer to these questions is &#8220;yes,&#8221; consider the following alternative metrics.<\/p>\n\n\n\n<h3 id=\"precision-when-false-positives-matter\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Precision_When_False_Positives_Matter\"><\/span><strong>Precision: When False Positives Matter<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Precision tells us how many of the predicted positive cases are correct. It is crucial in cases like spam detection, where marking an actual email as spam (false positive) can cause inconvenience.<\/p>\n\n\n\n<h3 id=\"recall-sensitivity-when-missing-positives-is-costly\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Recall_Sensitivity_When_Missing_Positives_is_Costly\"><\/span><strong>Recall (Sensitivity): When Missing Positives is Costly<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Recall measures how many actual positive cases were correctly identified. This is important in medical tests, where missing a patient with a disease (false negative) can have severe consequences.<\/p>\n\n\n\n<h3 id=\"f1-score-balancing-precision-and-recall\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"F1_Score_Balancing_Precision_and_Recall\"><\/span><strong>F1 Score: Balancing Precision and Recall<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The F1 score is a combination of precision and recall. It is useful when both false positives and false negatives need to be minimised, such as in fraud detection.<\/p>\n\n\n\n<h3 id=\"confusion-matrix-understanding-errors\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Confusion_Matrix_Understanding_Errors\"><\/span><strong>Confusion Matrix: Understanding Errors<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A <a href=\"https:\/\/pickl.ai\/blog\/an-introduction-to-understanding-confusion-matrix-in-machine-learning\/\">confusion matrix<\/a> shows how a model classifies data, breaking down errors into false positives and false negatives. This helps in analysing where the model is going wrong.<\/p>\n\n\n\n<h3 id=\"roc-curve-auc-measuring-trade-offs\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"ROC_Curve_AUC_Measuring_Trade-offs\"><\/span><strong>ROC Curve &amp; AUC: Measuring Trade-offs<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The <a href=\"https:\/\/pickl.ai\/blog\/auc-roc-curve-machine-learning\/\">ROC curve<\/a> shows the trade-off between correctly predicting positives (true positive rate) and incorrectly predicting negatives as positives (false positive rate). AUC (Area Under the Curve) measures the overall performance, with a higher value indicating a better model.<\/p>\n\n\n\n<h3 id=\"pr-curve-handling-imbalanced-data\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"PR-Curve_Handling_Imbalanced_Data\"><\/span><strong>PR-Curve: Handling Imbalanced Data<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The Precision-Recall (PR) curve is useful when one class is much smaller than the other, such as in rare disease detection. It shows how well a model distinguishes the minority class.<\/p>\n\n\n\n<h3 id=\"matthews-correlation-coefficient-a-balanced-metric\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Matthews_Correlation_Coefficient_A_Balanced_Metric\"><\/span><strong>Matthews Correlation Coefficient: A Balanced Metric<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>This <a href=\"https:\/\/bmcgenomics.biomedcentral.com\/articles\/10.1186\/s12864-019-6413-7\" rel=\"nofollow\">metric considers<\/a> all four outcomes (true positives, true negatives, false positives, and false negatives) and provides a single value that works well even for imbalanced datasets.<\/p>\n\n\n\n<p>Choosing the right evaluation metric ensures that your model performs well for its intended purpose rather than relying only on accuracy.<\/p>\n\n\n\n<h2 id=\"accuracy-in-multiclass-problems\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Accuracy_in_Multiclass_Problems\"><\/span><strong>Accuracy in Multiclass Problems<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Accuracy is a common way to measure how well a model predicts the correct answers. In multiclass classification, the model assigns each input to one of several categories. But is accuracy always the best way to measure performance? Let\u2019s break it down step by step.<\/p>\n\n\n\n<h3 id=\"what-is-accuracy-in-multiclass-classification\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_Accuracy_in_Multiclass_Classification\"><\/span><strong>What is Accuracy in Multiclass Classification?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Accuracy is the percentage of correct predictions made by the model. It is calculated using this formula:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXfQAZlaPiy_ADYh4oRUCRN1I5QTTl0bPeuEGBhbE_97Cbaqo9MMdaQFnQ_eGKgupvLsevphSZcFHDXw6K8iHYlTiyQyAn7Fjvp9DEEEk1qnhIRrSBurLWj9xqdF8Pre87UX69GOzw?key=m2HxfaXVHcLRwwa34XSqEC7I\" alt=\"Formula for calculating accuracy in multiclass classification.\u00a0\"\/><\/figure>\n\n\n\n<p>For example, if a model correctly predicts 80 out of 100 test cases, its accuracy is <strong>80%<\/strong>.<\/p>\n\n\n\n<h3 id=\"example-confusion-matrix-for-a-3-class-problem\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Example_Confusion_Matrix_for_a_3-Class_Problem\"><\/span><strong>Example: Confusion Matrix for a 3-Class Problem<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>A <strong>confusion matrix<\/strong> helps us understand how a model performs. Let\u2019s say we have three categories: <strong>A, B, and C<\/strong>. The table below shows a sample confusion matrix:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXfJ5kBKATVYOw40Hlr5f_ZsldDQ_AKeZdfUwCp5lDREhm46wn7uJrhECC_9aOitChCHFTqspawl5xE7c5F3xxKw_uRx_L7gHI8YOj_4zk0gbsKQD0oon4TL60bwMT957ho6K_HS?key=m2HxfaXVHcLRwwa34XSqEC7I\" alt=\"confusion matrix.\u00a0\"\/><\/figure>\n\n\n\n<p>Here, the correct predictions are <strong>30 (A), 25 (B), and 17 (C)<\/strong>. The total number of cases is <strong>100<\/strong>. So, accuracy is:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdvTc6ILCo2RyerX90xi-XKVn29s9rZkmJ_LXTVyPD3BwWCjBW4vxDTyM3V46If3QDDcf-JToHD_FAA6BDSoCt4V2AFW3xSN0ro3zkKAvzDCcDzkb7tUuV4DVLOasAqLhEJTuUM?key=m2HxfaXVHcLRwwa34XSqEC7I\" alt=\"Calculation of the accuracy.\"\/><\/figure>\n\n\n\n<h3 id=\"case-study-predicting-the-iris-dataset-with-a-decision-tree\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Case_Study_Predicting_the_Iris_Dataset_with_a_Decision_Tree\"><\/span><strong>Case Study: Predicting the Iris Dataset with a Decision Tree<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The <strong>Iris dataset<\/strong> contains flower measurements that classify flowers into three types: <strong>Setosa, Versicolor, and Virginica<\/strong>. A <strong>Decision Tree<\/strong> model analyses these measurements and makes predictions.<\/p>\n\n\n\n<p>Suppose we train a model on this dataset and achieve <strong>90% accuracy<\/strong>. This means the model predicts the correct flower type 90 times out of 100. However, does this mean the model is perfect? Not necessarily.<\/p>\n\n\n\n<h3 id=\"why-class-level-recall-is-more-informative\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Why_Class-Level_Recall_is_More_Informative\"><\/span><strong>Why Class-Level Recall is More Informative<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Sometimes, accuracy can be misleading. Imagine one class has <strong>way more examples<\/strong> than the others. The model might get high accuracy just by predicting the most common class more often.<\/p>\n\n\n\n<p><strong>Class-level recall<\/strong> tells us how well the model identifies each class separately. It shows if the model is unfairly favoring one class while ignoring others.<\/p>\n\n\n\n<p>For example, in medical diagnosis, if a model predicts <strong>\u201cno disease\u201d<\/strong> for most patients, accuracy may be high, but it\u2019s dangerous if real patients with diseases are missed. That\u2019s why recall is often more critical in multiclass classification.<\/p>\n\n\n\n<p>Would you trust a model that looks accurate or correctly identifies every class?<\/p>\n\n\n\n<h2 id=\"accuracy-in-multilabel-problems\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Accuracy_in_Multilabel_Problems\"><\/span><strong>Accuracy in Multilabel Problems<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>When working with Machine Learning, it\u2019s essential to understand the difference between <strong>multiclass<\/strong> and <strong>multilabel<\/strong> classification.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Multiclass classification<\/strong> means that each data point belongs to only one category. For example, if you classify animals, an image can be a dog, a cat, or a bird\u2014only one label applies.<\/li>\n\n\n\n<li><strong>Multilabel classification<\/strong> allows each data point to belong to multiple categories simultaneously. For example, an image of a forest may have labels like \u201ctrees,\u201d \u201criver,\u201d and \u201cwildlife\u201d all at once.<\/li>\n<\/ul>\n\n\n\n<p>This key difference makes multilabel problems more complex because each item has more than one correct answer.<\/p>\n\n\n\n<h3 id=\"challenges-in-multilabel-settings\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Challenges_in_Multilabel_Settings\"><\/span><strong>Challenges in Multilabel Settings<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Since multilabel classification assigns multiple labels to a single data point, several challenges arise:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Overlapping Labels<\/strong> \u2013 Some labels may frequently appear together, while others are rare. This imbalance can confuse the model.<\/li>\n\n\n\n<li><strong>Correlated Labels<\/strong> \u2013 Some labels have strong relationships. For example, in news classification, \u201cpolitics\u201d and \u201cgovernment\u201d often appear together.<\/li>\n\n\n\n<li><strong>Standard Accuracy Issues<\/strong> \u2013 Traditional accuracy measures, like checking if the entire set of labels is correct, do not work well because they do not consider partial correctness.<\/li>\n<\/ul>\n\n\n\n<h3 id=\"example-using-the-rcv1-dataset\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Example_Using_the_RCV1_Dataset\"><\/span><strong>Example Using the RCV1 Dataset<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The <strong>RCV1 (Reuters Corpus Volume 1) dataset<\/strong> is a well-known collection of news articles, where each article can belong to multiple categories like \u201cbusiness,\u201d \u201csports,\u201d or \u201ctechnology.\u201d If we use a standard accuracy measure, an article with three correct labels but missing one might be marked incorrect, which is misleading.<\/p>\n\n\n\n<h3 id=\"why-standard-accuracy-is-misleading-in-multilabel-problems\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Why_Standard_Accuracy_is_Misleading_in_Multilabel_Problems\"><\/span><strong>Why Standard Accuracy is Misleading in Multilabel Problems<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>In multiclass classification, accuracy is simple: If the predicted label matches the actual label, it&#8217;s correct. But in multilabel classification, things are different.<\/p>\n\n\n\n<p>For example, if an article has three correct labels, but the model predicts only two, should we say the entire prediction is wrong? Standard accuracy does this, which is unfair. We need a better way to measure performance that accounts for partial correctness.<\/p>\n\n\n\n<h3 id=\"subset-accuracy-exact-match-ratio-in-multi-label-classification\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Subset_Accuracy_Exact_Match_Ratio_in_Multi-Label_Classification\"><\/span><strong>Subset Accuracy (Exact Match Ratio) in Multi-Label Classification<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Evaluating the model&#8217;s performance can be tricky when working with multi-label classification. Subset Accuracy is one of the strictest evaluation methods, also called the Exact Match Ratio. Let&#8217;s explore what it means, why it can be challenging, and better alternatives for evaluation.<\/p>\n\n\n\n<h3 id=\"what-is-subset-accuracy\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_Subset_Accuracy\"><\/span><strong>What is Subset Accuracy?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Subset Accuracy measures how often a model correctly predicts the exact set of labels for a given input. It checks if all predicted labels perfectly match the actual labels. The entire prediction is considered wrong if even one label is incorrect or missing.<\/p>\n\n\n\n<p>For example, imagine a model that identifies objects in images. If an image contains a <strong>cat, dog, and ball<\/strong>, and the model predicts <strong>cat and ball<\/strong>, the prediction is considered incorrect\u2014even though it got two out of three labels right.<\/p>\n\n\n\n<h3 id=\"challenges-with-subset-accuracy\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Challenges_with_Subset_Accuracy\"><\/span><strong>Challenges with Subset Accuracy<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Subset Accuracy is very strict. In real-world scenarios, models rarely predict all labels with 100% accuracy, especially when dealing with complex data. This makes Subset Accuracy less practical because:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>It gives zero credit for partial correctness:<\/strong> A single mistake is a failure, even if most labels are correct.<\/li>\n\n\n\n<li><strong>It is sensitive to small errors:<\/strong> A minor misclassification can drastically lower the score.<\/li>\n\n\n\n<li><strong>It is not ideal for large label sets:<\/strong> Achieving perfect predictions is highly unlikely when there are many possible labels.<\/li>\n<\/ul>\n\n\n\n<p>Due to these challenges, other evaluation metrics provide a more balanced view of a model&#8217;s performance.<\/p>\n\n\n\n<h3 id=\"better-alternatives-for-evaluating-multi-label-models\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Better_Alternatives_for_Evaluating_Multi-Label_Models\"><\/span><strong>Better Alternatives for Evaluating Multi-Label Models<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Since Subset Accuracy can be too harsh, here are some better ways to measure how well a model performs:<\/p>\n\n\n\n<h3 id=\"hamming-score\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Hamming_Score\"><\/span><strong>Hamming Score<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Hamming Score calculates the percentage of correctly predicted labels out of all labels. Unlike Subset Accuracy, it gives partial credit when some labels are correct.<\/p>\n\n\n\n<p>For example, if the actual labels are <strong>cat, dog, ball<\/strong>, and the model predicts <strong>cat and ball<\/strong>, the Hamming Score considers this partly correct instead of entirely wrong.<\/p>\n\n\n\n<h3 id=\"hamming-loss\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Hamming_Loss\"><\/span><strong>Hamming Loss<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Hamming Loss measures the number of incorrect label predictions. A lower Hamming Loss means a better-performing model. It helps identify how often mistakes happen rather than whether an entire set is correct or incorrect.<\/p>\n\n\n\n<h3 id=\"precision-recall-and-f1-score\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Precision_Recall_and_F1_Score\"><\/span><strong>Precision, Recall, and F1 Score<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>These three metrics are commonly used for multi-label classification:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Precision:<\/strong> Measures how many of the predicted labels are correct.<\/li>\n\n\n\n<li><strong>Recall:<\/strong> Measures how many actual labels were correctly predicted.<\/li>\n\n\n\n<li><strong>F1 Score:<\/strong> Balances Precision and Recall to give a single performance score.<\/li>\n<\/ul>\n\n\n\n<p>Using these alternatives, you get a clearer and fairer evaluation of how well a model predicts multiple labels rather than just checking for an exact match.<\/p>\n\n\n\n<h2 id=\"additional-accuracy-metrics\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Additional_Accuracy_Metrics\"><\/span><strong>Additional Accuracy Metrics<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXdB7FSRKMyqkNIeyw6Nmh2i_aOUZTbkfT-zXH7MMsvQReXUJJ-MFSMvH_WGt8OsJaEKnJK4cfjaZPJxMBFxayzeM1y5c_z6xHC_fE5I5_4xvOZ_QUvUpqXDKVNyDoHeaPRshkZG?key=m2HxfaXVHcLRwwa34XSqEC7I\" alt=\"accuracy metrics.\"\/><\/figure>\n\n\n\n<p>While accuracy is essential in evaluating a model\u2019s performance, it doesn\u2019t always give the whole picture. In some cases, like when dealing with imbalanced data or probability-based predictions, other accuracy metrics provide better insights. Let\u2019s explore three key accuracy metrics:<\/p>\n\n\n\n<h3 id=\"balanced-accuracy-handling-imbalanced-datasets\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Balanced_Accuracy_Handling_Imbalanced_Datasets\"><\/span><strong>Balanced Accuracy: Handling Imbalanced Datasets<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Sometimes, datasets are imbalanced, meaning one category has far more examples than another.<\/p>\n\n\n\n<p>For example, in a medical test for a rare disease, most results will be negative, making a simple accuracy score misleading. Balanced accuracy solves this problem by giving equal importance to common and rare cases.&nbsp;<\/p>\n\n\n\n<p>It takes the average of two values: how well the model identifies positive cases and how well it identifies negative cases. This ensures a fair evaluation, even when one group is much larger than the other.<\/p>\n\n\n\n<h3 id=\"top-k-accuracy-useful-for-recommendations-and-image-recognition\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Top-K_Accuracy_Useful_for_Recommendations_and_Image_Recognition\"><\/span><strong>Top-K Accuracy: Useful for Recommendations and Image Recognition<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Imagine searching for a movie recommendation. A system may suggest multiple movies, and the recommendation is helpful if at least one of them is what you like. This is where <strong>Top-K Accuracy<\/strong> comes in.&nbsp;<\/p>\n\n\n\n<p>Instead of checking if the model\u2019s top answer is correct, it checks if the correct answer appears within the top K (e.g., top 3 or top 5) predictions. This metric is critical in recommendation systems (like Netflix or Spotify) and image classification (where multiple objects may be in a photo).<\/p>\n\n\n\n<h3 id=\"accuracy-of-probability-predictions-measuring-confidence-in-predictions\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Accuracy_of_Probability_Predictions_Measuring_Confidence_in_Predictions\"><\/span><strong>Accuracy of Probability Predictions: Measuring Confidence in Predictions<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Some models don\u2019t just make a yes-or-no decision\u2014they provide probabilities. For example, a weather app might say there\u2019s a <strong>70% chance of rain<\/strong> instead of just predicting \u201crain\u201d or \u201cno rain.\u201d&nbsp;<\/p>\n\n\n\n<p>We use<strong> <\/strong><a href=\"https:\/\/www.dratings.com\/log-loss-vs-brier-score\/\" rel=\"nofollow\"><strong>Log Loss and Brier Score<\/strong><\/a> to check how well these probability-based models perform. Log Loss measures how close the predicted probabilities are to the actual outcomes, with lower values indicating better predictions. Brier Score does something similar but is easier to understand\u2014lower scores mean more accurate probability estimates.<\/p>\n\n\n\n<h2 id=\"in-the-end\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"In_The_End\"><\/span><strong>In The End<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Ensuring the accuracy of a Machine Learning model is crucial for reliable predictions and decision-making. By employing techniques such as cross-validation, metrics like precision and recall, and visualizations like ROC curves, you can comprehensively evaluate your model&#8217;s performance.&nbsp;<\/p>\n\n\n\n<p>Regularly testing and refining your model against diverse datasets helps maintain its accuracy over time. Implementing these strategies not only enhances model reliability but also fosters trust in AI-driven insights, leading to better business outcomes and more informed decision-making processes.&nbsp;<\/p>\n\n\n\n<p>By prioritising model accuracy, you set the foundation for successful AI integration across various industries.<\/p>\n\n\n\n<h2 id=\"frequently-asked-questions\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><strong>Frequently Asked Questions<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 id=\"what-is-accuracy-in-machine-learning-and-why-is-it-important\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_Accuracy_in_Machine_Learning_and_Why_is_it_Important\"><\/span><strong>What is Accuracy in Machine Learning, and Why is it Important?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Accuracy in Machine Learning calculates the proportion of correct predictions relative to total predictions. It offers an initial measure of model performance and is easy to interpret. However, accuracy may hide underlying issues in imbalanced datasets, making additional metrics necessary to assess a model\u2019s effectiveness and reliability for full evaluation.<\/p>\n\n\n\n<h3 id=\"why-can-accuracy-in-machine-learning-be-misleading-in-certain-scenarios\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Why_can_Accuracy_in_Machine_Learning_be_Misleading_in_Certain_Scenarios\"><\/span><strong>Why can Accuracy in Machine Learning be Misleading in Certain Scenarios?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Accuracy in Machine Learning can be misleading when data is imbalanced or errors carry high consequences. A model might achieve high accuracy by favoring a majority class while failing to detect minority cases. This oversight can hide deficiencies, emphasising the need to consider precision, recall, and other evaluation metrics.<\/p>\n\n\n\n<h3 id=\"what-alternative-metrics-should-i-use-instead-of-relying-solely-on-accuracy-in-machine-learning\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_Alternative_Metrics_Should_I_Use_Instead_of_Relying_Solely_on_Accuracy_in_Machine_Learning\"><\/span><strong>What Alternative Metrics Should I Use Instead of Relying Solely on Accuracy in Machine Learning?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Instead of relying solely on accuracy in Machine Learning, consider using precision, recall, and F1-score to assess performance more thoroughly. Use confusion matrices, ROC curves, and balanced accuracy for imbalanced datasets. These alternative metrics provide deeper insights into errors, model reliability, and true performance across diverse scenarios for evaluation.<\/p>\n","protected":false},"excerpt":{"rendered":"Master accuracy in Machine Learning with robust evaluation metrics for top model performance!!\n","protected":false},"author":4,"featured_media":20287,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[2],"tags":[3811],"ppma_author":[2169,2631],"class_list":{"0":"post-20285","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-machine-learning","8":"tag-accuracy-in-machine-learning"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>How to Measure Accuracy in Machine Learning Models<\/title>\n<meta name=\"description\" content=\"Learn why accuracy in Machine Learning can be misleading. 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