{"id":20295,"date":"2025-03-06T18:57:28","date_gmt":"2025-03-06T18:57:28","guid":{"rendered":"https:\/\/www.pickl.ai\/blog\/?p=20295"},"modified":"2025-03-06T18:57:29","modified_gmt":"2025-03-06T18:57:29","slug":"softmax-regression","status":"publish","type":"post","link":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/","title":{"rendered":"How Softmax Regression Works: A Step-by-Step Tutorial"},"content":{"rendered":"\n<p><strong>Summary: <\/strong>This tutorial provides a comprehensive guide on Softmax Regression, explaining its principles and implementation using NumPy and PyTorch. It covers the softmax function, cross-entropy loss, and training process, making it suitable for beginners and experienced learners alike.<\/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\/softmax-regression\/#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\/softmax-regression\/#Introduction_to_Softmax_Regression\" >Introduction to Softmax Regression<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#How_Softmax_Regression_Works\" >How Softmax Regression Works<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Linear_Transformation\" >Linear Transformation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Softmax_Function\" >Softmax Function<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Prediction\" >Prediction<\/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\/softmax-regression\/#Training\" >Training<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Example_Walkthrough\" >Example Walkthrough<\/a><\/li><\/ul><\/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\/softmax-regression\/#Implementing_Softmax_Regression_in_Python\" >Implementing Softmax Regression in Python<\/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\/softmax-regression\/#Using_NumPy\" >Using NumPy<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Step_1_Define_the_Softmax_Function\" >Step 1: Define the Softmax Function<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Step_2_Define_the_Cross-Entropy_Loss\" >Step 2: Define the Cross-Entropy Loss<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Step_3_Train_the_Model_Using_Gradient_Descent\" >Step 3: Train the Model Using Gradient Descent<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Advantages_of_Softmax_Regression\" >Advantages of Softmax Regression<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Simple_and_Interpretable\" >Simple and Interpretable<\/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\/softmax-regression\/#Efficient_Training\" >Efficient Training<\/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\/softmax-regression\/#Good_for_Linearly_Separable_Data\" >Good for Linearly Separable Data<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Feature_Importance\" >Feature Importance<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Flexibility_in_Model_Complexity\" >Flexibility in Model Complexity<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Applications_of_Softmax_Regression\" >Applications of Softmax Regression<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Image_Classification\" >Image Classification<\/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\/softmax-regression\/#Natural_Language_Processing_NLP\" >Natural Language Processing (NLP)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Medical_Diagnosis\" >Medical Diagnosis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Ecology_and_Biology\" >Ecology and Biology<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#Concluding_Thoughts\" >Concluding Thoughts<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#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-27\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#What_is_the_Main_Difference_Between_Softmax_Regression_and_Logistic_Regression\" >What is the Main Difference Between Softmax Regression and Logistic Regression?<\/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\/softmax-regression\/#How_Does_The_Softmax_Function_Ensure_Probabilities_Sum_to_1\" >How Does The Softmax Function Ensure Probabilities Sum to 1?<\/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\/softmax-regression\/#What_Optimization_Algorithm_is_Helpful_in_Training_Softmax_Regression_Models\" >What Optimization Algorithm is Helpful in Training Softmax Regression Models?<\/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>The expansive world of <a href=\"https:\/\/pickl.ai\/blog\/boosting-in-machine-learning\/\">Machine Learning <\/a>offers an arsenal of tools, Softmax Regression is one such powerful tool for tackling multi-class classification problems. Whether you&#8217;re a seasoned data scientist or just starting your journey in AI, understanding how Softmax Regression works is crucial for building robust models that can accurately predict outcomes across multiple categories.<\/p>\n\n\n\n<p>This tutorial will guide you through the ins and outs of Softmax Regression, including its implementation in Python, making it an indispensable resource for anyone looking to enhance their Machine Learning skills.<\/p>\n\n\n\n<h2 id=\"introduction-to-softmax-regression\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Introduction_to_Softmax_Regression\"><\/span><strong>Introduction to Softmax Regression<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Softmax Regression aka multinomial logistic regression, is an extension of binary logistic regression. It handles scenarios where data points can belong to more than two classes.<\/p>\n\n\n\n<p>Some of its key applications include image classification, text categorization, and more. The core idea behind Softmax is to compute the probability of an input belonging to each class and then predict the class with the highest probability.<\/p>\n\n\n\n<h2 id=\"how-softmax-regression-works\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_Softmax_Regression_Works\"><\/span><strong>How Softmax Regression Works<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Softmax Regression, also known as multinomial logistic regression, is a Machine Learning technique used for multiclass classification problems. It is an extension of binary logistic regression, designed to handle scenarios where the goal is to assign input data points to multiple classes. Here\u2019s a step-by-step explanation of how Softmax  works:<\/p>\n\n\n\n<h3 id=\"linear-transformation\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Linear_Transformation\"><\/span><strong>Linear Transformation<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>Weighted Sum: <\/strong>To calculate the linear combination for each class, we use class-specific weights and a bias term. Here is the representation of the same:<\/p>\n\n\n\n<p>zi=Wi\u22c5x+bi<em>zi<\/em>=<em>Wi<\/em>\u22c5<em>x<\/em>+<em>bi<\/em>,&nbsp;<\/p>\n\n\n\n<p>Here&nbsp; zi<em>zi<\/em> is the linear combination for class i<em>i<\/em>, Wi<em>Wi<\/em> is the weight matrix for class i<em>i<\/em>, x<em>x<\/em> is the input feature vector, and bi<em>bi<\/em> is the bias term for class i<em>i<\/em>.<\/p>\n\n\n\n<h3 id=\"softmax-function\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Softmax_Function\"><\/span><strong>Softmax Function<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>Probability Calculation: <\/strong>We use the softmax function to convert them into probabilities. Here is the calculation for class i<em>i<\/em>:<\/p>\n\n\n\n<p>P(y=i\u2223x)=exp\u2061(zi)\u2211j=1Kexp\u2061(zj)<em>P<\/em>(<em>y<\/em>=<em>i<\/em>\u2223<em>x<\/em>)=\u2211<em>j<\/em>=1<em>K<\/em>exp(<em>zj<\/em>)exp(<em>zi<\/em>)<\/p>\n\n\n\n<p>where zi<em>zi<\/em> is the linear combination for class i<em>i<\/em>, and the sum in the denominator is the over all classes j<em>j<\/em>. This ensures that the probabilities for all classes sum up to 1.<\/p>\n\n\n\n<h3 id=\"prediction\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Prediction\"><\/span><strong>Prediction<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>Class Selection:<\/strong> The class with the highest probability is selected as the predicted class. Here is its mathematical representation:<\/p>\n\n\n\n<p>ypred=arg\u2061max\u2061iP(y=i\u2223x)<em>y<\/em>pred=arg<em>i<\/em>max<em>P<\/em>(<em>y<\/em>=<em>i<\/em>\u2223<em>x<\/em>)<\/p>\n\n\n\n<p>for all classes i<em>i<\/em>.<\/p>\n\n\n\n<h3 id=\"training\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Training\"><\/span><strong>Training<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>Loss Function:<\/strong> For minimization during training, Softmax  typically uses cross-entropy loss. The cross-entropy loss measures the difference between the predicted probabilities and the actual class labels.<\/p>\n\n\n\n<p><strong>Optimization:<\/strong> We can minimize the loss function or update the model using the gradient descent or other optimization algorithms.<\/p>\n\n\n\n<h4 id=\"example-walkthrough\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Example_Walkthrough\"><\/span><strong>Example Walkthrough<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>Consider a simple example with three classes (0, 1, and 2) and two input features (x1<em>x<\/em>1 and x2<em>x<\/em>2). The goal is to predict the class for a new input.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Linear Transformation:<\/strong> Compute zi=Wi\u22c5[x1,x2]+bi<em>zi<\/em>=<em>Wi<\/em>\u22c5[<em>x<\/em>1,<em>x<\/em>2]+<em>bi<\/em> for each class.<\/li>\n\n\n\n<li><strong>Softmax Function:<\/strong> Apply the softmax function to zi<em>zi<\/em> values to get probabilities for each class.<\/li>\n\n\n\n<li><strong>Prediction<\/strong>: Choose the class with the highest probability.<\/li>\n<\/ul>\n\n\n\n<h2 id=\"implementing-softmax-regression-in-python\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Implementing_Softmax_Regression_in_Python\"><\/span><strong>Implementing Softmax Regression in Python<\/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_4nXdIPc3LTsiXCGS-DvSfuj1Fja-yKubNWyQZ9DcFVgXL4-X9HxF02azjskrY0TrsfQs5uOwIIlrdZixKBEc_Zc7bPInef99tsT8Up6UQRAqrrVrtndnDXfsnwBKIAUUaoyab6AjcTA?key=PuA_B0RFJW74rOxHsfWknRFD\" alt=\"implementation of Softmax Regression in Python\"\/><\/figure>\n\n\n\n<p>Softmax Regression is a powerful tool for multi-class classification problems, widely used in Machine Learning applications such as image classification and text analysis. Here&#8217;s a step-by-step guide on how to implement Softmax <a href=\"https:\/\/pickl.ai\/blog\/linear-regression-in-machine-learning\/\">Regression<\/a> in Python using both NumPy and PyTorch.<\/p>\n\n\n\n<h3 id=\"using-numpy\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Using_NumPy\"><\/span><strong>Using NumPy<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Implementing Softmax Regression from scratch using NumPy involves defining the softmax function and the cross-entropy loss, then training the model using gradient descent.<\/p>\n\n\n\n<h4 id=\"step-1-define-the-softmax-function\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_1_Define_the_Softmax_Function\"><\/span><strong>Step 1: Define the Softmax Function<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>The softmax function converts the input vector into a probability distribution. It is defined as:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcDlXDpw4Q3h2lImffvr4KI6KCDdPLmTgevXDYLIE44tVMmpOny0gwgvTCUyKaoRrpce1nGEMP732Pbr266DXeiaNQb1sVbLQpDENO-JPeqTjn5sQKoF74-LWVuo5cBvaIECGXcmA?key=PuA_B0RFJW74rOxHsfWknRFD\" alt=\" Softmax Function\"\/><\/figure>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXfHmeYjBZDSQ-DMBaoWBzCMCGXomOTrLxr08e-yUTIqFGD4vZKwIknY7dT3nISEYpoglI8wv27pWZcfNWzQUX7AJeXlxMs2hfngs7cL-XFZEqqWyqzWHtA7v6UmW6vjUv1Md17QqA?key=PuA_B0RFJW74rOxHsfWknRFD\" alt=\"Softmax Function use to convert vector to probability distribution\"\/><\/figure>\n\n\n\n<h4 id=\"step-2-define-the-cross-entropy-loss\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_2_Define_the_Cross-Entropy_Loss\"><\/span><strong>Step 2: Define the Cross-Entropy Loss<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>To measure the difference between predicted probabilities and true labels, we use The cross-entropy loss.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/lh7-rt.googleusercontent.com\/docsz\/AD_4nXcz6v2HbcF4UnELlJS_arsZJ8moLI9UndiGyPTj4HDkqGC7SN3odXdDgSOBzUNZNceBGGdiTF-gfVyxXb3jByrgKEI8UJmak2nUArZeXbxozux9atkNJUQOuHGZLJAYCXmFf-p1?key=PuA_B0RFJW74rOxHsfWknRFD\" alt=\" Image showing cross-entropy loss\"\/><\/figure>\n\n\n\n<h4 id=\"step-3-train-the-model-using-gradient-descent\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Step_3_Train_the_Model_Using_Gradient_Descent\"><\/span><strong>Step 3: Train the Model Using Gradient Descent<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Load and Prepare Data:<\/strong> Load a dataset like Iris and split it into training and testing sets.<\/li>\n\n\n\n<li><strong>Initialise Weights and Biases<\/strong>: Initialise weights and biases randomly.<\/li>\n\n\n\n<li><strong>Training Loop:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Compute scores using z=Wx+b<em>z<\/em>=<em>Wx<\/em>+<em>b<\/em>.<\/li>\n\n\n\n<li>Apply softmax to get probabilities.<\/li>\n\n\n\n<li>Compute cross-entropy loss.<\/li>\n\n\n\n<li>Backpropagate gradients to update weights and biases.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<h2 id=\"advantages-of-softmax-regression\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Advantages_of_Softmax_Regression\"><\/span><strong>Advantages of Softmax Regression<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Softmax Regression, also known as multinomial logistic regression, is a powerful tool for handling multiclass classification problems. It offers several advantages that make it a popular choice in Machine Learning:<\/p>\n\n\n\n<h3 id=\"simple-and-interpretable\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Simple_and_Interpretable\"><\/span><strong>Simple and Interpretable<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Softmax Regression is a straightforward extension of logistic regression, which makes it easy to understand and interpret the output probabilities. This simplicity allows for a smooth transition from binary classification problems to multiclass scenarios.<\/p>\n\n\n\n<p>The output of Softmax is a probability distribution over all classes. This provides not only the most likely class but also the confidence in that prediction, which is valuable in many applications.<\/p>\n\n\n\n<h3 id=\"efficient-training\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Efficient_Training\"><\/span><strong>Efficient Training<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Optimization techniques like gradient descent are useful for efficient training. This is particularly important for large datasets, where computational efficiency is crucial. Softmax Regression can handle large datasets and is scalable, making it suitable for real-world applications where data volumes are high.<\/p>\n\n\n\n<h3 id=\"good-for-linearly-separable-data\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Good_for_Linearly_Separable_Data\"><\/span><strong>Good for Linearly Separable Data<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Softmax Regression performs well when classes are reasonably well-separated by linear boundaries. This makes it effective in scenarios where the classes have distinct features and have linear decision boundaries.<\/p>\n\n\n\n<h3 id=\"feature-importance\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Feature_Importance\"><\/span><strong>Feature Importance<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The learned weights in Softmax Regression help understand which features contribute more to classification decisions. This can be useful for feature selection and understanding the underlying relationships between features and classes.<\/p>\n\n\n\n<h3 id=\"flexibility-in-model-complexity\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Flexibility_in_Model_Complexity\"><\/span><strong>Flexibility in Model Complexity<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Softmax Regression can be combined with regularization techniques (like L1 or L2 regularization) to control model complexity and prevent overfitting.<\/p>\n\n\n\n<h2 id=\"applications-of-softmax-regression\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Applications_of_Softmax_Regression\"><\/span><strong>Applications of Softmax Regression<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Softmax Regression, also known as multinomial logistic regression, finds application in various fields. This is due to its effectiveness in solving multiclass classification problems. Here are some of its key applications:<\/p>\n\n\n\n<h3 id=\"image-classification\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Image_Classification\"><\/span><strong>Image Classification<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Classifying images into multiple categories, such as objects in a scene (e.g., cars, trees, buildings).Recognizing handwritten digits in applications like OCR (Optical Character Recognition).<\/p>\n\n\n\n<h3 id=\"natural-language-processing-nlp\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Natural_Language_Processing_NLP\"><\/span><strong>Natural Language Processing (NLP)<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Classifying text documents into predefined classes, such as spam detection, sentiment analysis, or topic classification. Assigning each word in a sentence a specific part of speech (e.g., noun, verb, adjective). Predicting the next word in a sequence based on context.<\/p>\n\n\n\n<h3 id=\"medical-diagnosis\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Medical_Diagnosis\"><\/span><strong>Medical Diagnosis<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Another key application of Softmax Regression is in the classification of the patient data. With this, we can easily classify the patient data into different categories based on the symptoms, lab results, and medical history. Identifying types of tumors (benign vs. malignant) based on medical imaging and patient data..<\/p>\n\n\n\n<h3 id=\"ecology-and-biology\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Ecology_and_Biology\"><\/span><strong>Ecology and Biology<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>With the use of this tool, we can easily classify species based on ecological features or genetic data. Identifying suitable habitats for different species based on environmental conditions.<\/p>\n\n\n\n<h2 id=\"concluding-thoughts\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Concluding_Thoughts\"><\/span><strong>Concluding Thoughts<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In conclusion, Softmax Regression is a powerful tool in Machine Learning that enables efficient multi-class classification. By understanding its mechanics and <a href=\"https:\/\/pickl.ai\/blog\/factorial-program-in-python\/\">implementing it in Python<\/a>, you can tackle complex classification tasks with ease.<\/p>\n\n\n\n<p>Whether you&#8217;re working on image recognition or text analysis, mastering Softmax Regression will enhance your ability to build robust and accurate models.<\/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-the-main-difference-between-softmax-regression-and-logistic-regression\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_is_the_Main_Difference_Between_Softmax_Regression_and_Logistic_Regression\"><\/span><strong>What is the Main Difference Between Softmax Regression and Logistic Regression?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Softmax Regression finds application in Multi-class classification, while logistic regression is apt for binary classification.<\/p>\n\n\n\n<h3 id=\"how-does-the-softmax-function-ensure-probabilities-sum-to-1\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"How_Does_The_Softmax_Function_Ensure_Probabilities_Sum_to_1\"><\/span><strong>How Does The Softmax Function Ensure Probabilities Sum to 1?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The softmax function normalizes the exponential of each linear combination by dividing by the sum of all exponentials, ensuring the probabilities sum to 1.<\/p>\n\n\n\n<h3 id=\"what-optimization-algorithm-is-helpful-in-training-softmax-regression-models\" class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_Optimization_Algorithm_is_Helpful_in_Training_Softmax_Regression_Models\"><\/span><strong>What Optimization Algorithm is Helpful in Training Softmax Regression Models?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Gradient descent is applicable to minimise the cross-entropy loss and train Softmax models.<\/p>\n","protected":false},"excerpt":{"rendered":"Step-by-step guide to Softmax Regression with NumPy and PyTorch implementations.\n","protected":false},"author":4,"featured_media":20296,"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":[25,3817],"ppma_author":[2169,2606],"class_list":{"0":"post-20295","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-machine-learning","8":"tag-machine-learning","9":"tag-softmax-regression"},"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>Softmax Regression: The Key to Multi-Class Classification<\/title>\n<meta name=\"description\" content=\"Learn how Softmax Regression works with a step-by-step tutorial, covering its application and implementation using NumPy and PyTorch.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How Softmax Regression Works: A Step-by-Step Tutorial\" \/>\n<meta property=\"og:description\" content=\"Learn how Softmax Regression works with a step-by-step tutorial, covering its application and implementation using NumPy and PyTorch.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.pickl.ai\/blog\/softmax-regression\/\" \/>\n<meta property=\"og:site_name\" content=\"Pickl.AI\" \/>\n<meta property=\"article:published_time\" content=\"2025-03-06T18:57:28+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-03-06T18:57:29+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2025\/03\/unnamed-15.png\" \/>\n\t<meta property=\"og:image:width\" content=\"800\" \/>\n\t<meta property=\"og:image:height\" content=\"500\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Neha Singh, Antara Mandal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Neha Singh\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/\"},\"author\":{\"name\":\"Neha Singh\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/#\\\/schema\\\/person\\\/2ad633a6bc1b93bc13591b60895be308\"},\"headline\":\"How Softmax Regression Works: A Step-by-Step Tutorial\",\"datePublished\":\"2025-03-06T18:57:28+00:00\",\"dateModified\":\"2025-03-06T18:57:29+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/\"},\"wordCount\":1285,\"commentCount\":0,\"image\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/wp-content\\\/uploads\\\/2025\\\/03\\\/unnamed-15.png\",\"keywords\":[\"Machine Learning\",\"softmax regression\"],\"articleSection\":[\"Machine Learning\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/\",\"url\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/\",\"name\":\"Softmax Regression: The Key to Multi-Class Classification\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/wp-content\\\/uploads\\\/2025\\\/03\\\/unnamed-15.png\",\"datePublished\":\"2025-03-06T18:57:28+00:00\",\"dateModified\":\"2025-03-06T18:57:29+00:00\",\"author\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/#\\\/schema\\\/person\\\/2ad633a6bc1b93bc13591b60895be308\"},\"description\":\"Learn how Softmax Regression works with a step-by-step tutorial, covering its application and implementation using NumPy and PyTorch.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/#primaryimage\",\"url\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/wp-content\\\/uploads\\\/2025\\\/03\\\/unnamed-15.png\",\"contentUrl\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/wp-content\\\/uploads\\\/2025\\\/03\\\/unnamed-15.png\",\"width\":800,\"height\":500,\"caption\":\"How Softmax Regression Works\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/softmax-regression\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Machine Learning\",\"item\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/category\\\/machine-learning\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"How Softmax Regression Works: A Step-by-Step Tutorial\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/#website\",\"url\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/\",\"name\":\"Pickl.AI\",\"description\":\"\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/#\\\/schema\\\/person\\\/2ad633a6bc1b93bc13591b60895be308\",\"name\":\"Neha Singh\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/pickl.ai\\\/blog\\\/wp-content\\\/uploads\\\/2024\\\/06\\\/avatar_user_4_1717572961-96x96.jpg3d1a0d35d7a1a929f4a120e9053cbdb5\",\"url\":\"https:\\\/\\\/pickl.ai\\\/blog\\\/wp-content\\\/uploads\\\/2024\\\/06\\\/avatar_user_4_1717572961-96x96.jpg\",\"contentUrl\":\"https:\\\/\\\/pickl.ai\\\/blog\\\/wp-content\\\/uploads\\\/2024\\\/06\\\/avatar_user_4_1717572961-96x96.jpg\",\"caption\":\"Neha Singh\"},\"description\":\"I\u2019m a full-time freelance writer and editor who enjoys wordsmithing. The 8 years long journey as a content writer and editor has made me relaize the significance and power of choosing the right words. Prior to my writing journey, I was a trainer and human resource manager. WIth more than a decade long professional journey, I find myself more powerful as a wordsmith. As an avid writer, everything around me inspires me and pushes me to string words and ideas to create unique content; and when I\u2019m not writing and editing, I enjoy experimenting with my culinary skills, reading, gardening, and spending time with my adorable little mutt Neel.\",\"url\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/author\\\/nehasingh\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Softmax Regression: The Key to Multi-Class Classification","description":"Learn how Softmax Regression works with a step-by-step tutorial, covering its application and implementation using NumPy and PyTorch.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/","og_locale":"en_US","og_type":"article","og_title":"How Softmax Regression Works: A Step-by-Step Tutorial","og_description":"Learn how Softmax Regression works with a step-by-step tutorial, covering its application and implementation using NumPy and PyTorch.","og_url":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/","og_site_name":"Pickl.AI","article_published_time":"2025-03-06T18:57:28+00:00","article_modified_time":"2025-03-06T18:57:29+00:00","og_image":[{"width":800,"height":500,"url":"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2025\/03\/unnamed-15.png","type":"image\/png"}],"author":"Neha Singh, Antara Mandal","twitter_card":"summary_large_image","twitter_misc":{"Written by":"Neha Singh","Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#article","isPartOf":{"@id":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/"},"author":{"name":"Neha Singh","@id":"https:\/\/www.pickl.ai\/blog\/#\/schema\/person\/2ad633a6bc1b93bc13591b60895be308"},"headline":"How Softmax Regression Works: A Step-by-Step Tutorial","datePublished":"2025-03-06T18:57:28+00:00","dateModified":"2025-03-06T18:57:29+00:00","mainEntityOfPage":{"@id":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/"},"wordCount":1285,"commentCount":0,"image":{"@id":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#primaryimage"},"thumbnailUrl":"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2025\/03\/unnamed-15.png","keywords":["Machine Learning","softmax regression"],"articleSection":["Machine Learning"],"inLanguage":"en-US","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/www.pickl.ai\/blog\/softmax-regression\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/","url":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/","name":"Softmax Regression: The Key to Multi-Class Classification","isPartOf":{"@id":"https:\/\/www.pickl.ai\/blog\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#primaryimage"},"image":{"@id":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#primaryimage"},"thumbnailUrl":"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2025\/03\/unnamed-15.png","datePublished":"2025-03-06T18:57:28+00:00","dateModified":"2025-03-06T18:57:29+00:00","author":{"@id":"https:\/\/www.pickl.ai\/blog\/#\/schema\/person\/2ad633a6bc1b93bc13591b60895be308"},"description":"Learn how Softmax Regression works with a step-by-step tutorial, covering its application and implementation using NumPy and PyTorch.","breadcrumb":{"@id":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.pickl.ai\/blog\/softmax-regression\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#primaryimage","url":"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2025\/03\/unnamed-15.png","contentUrl":"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2025\/03\/unnamed-15.png","width":800,"height":500,"caption":"How Softmax Regression Works"},{"@type":"BreadcrumbList","@id":"https:\/\/www.pickl.ai\/blog\/softmax-regression\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/www.pickl.ai\/blog\/"},{"@type":"ListItem","position":2,"name":"Machine Learning","item":"https:\/\/www.pickl.ai\/blog\/category\/machine-learning\/"},{"@type":"ListItem","position":3,"name":"How Softmax Regression Works: A Step-by-Step Tutorial"}]},{"@type":"WebSite","@id":"https:\/\/www.pickl.ai\/blog\/#website","url":"https:\/\/www.pickl.ai\/blog\/","name":"Pickl.AI","description":"","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/www.pickl.ai\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/www.pickl.ai\/blog\/#\/schema\/person\/2ad633a6bc1b93bc13591b60895be308","name":"Neha Singh","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2024\/06\/avatar_user_4_1717572961-96x96.jpg3d1a0d35d7a1a929f4a120e9053cbdb5","url":"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2024\/06\/avatar_user_4_1717572961-96x96.jpg","contentUrl":"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2024\/06\/avatar_user_4_1717572961-96x96.jpg","caption":"Neha Singh"},"description":"I\u2019m a full-time freelance writer and editor who enjoys wordsmithing. The 8 years long journey as a content writer and editor has made me relaize the significance and power of choosing the right words. Prior to my writing journey, I was a trainer and human resource manager. WIth more than a decade long professional journey, I find myself more powerful as a wordsmith. As an avid writer, everything around me inspires me and pushes me to string words and ideas to create unique content; and when I\u2019m not writing and editing, I enjoy experimenting with my culinary skills, reading, gardening, and spending time with my adorable little mutt Neel.","url":"https:\/\/www.pickl.ai\/blog\/author\/nehasingh\/"}]}},"jetpack_featured_media_url":"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2025\/03\/unnamed-15.png","authors":[{"term_id":2169,"user_id":4,"is_guest":0,"slug":"nehasingh","display_name":"Neha Singh","avatar_url":"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2024\/06\/avatar_user_4_1717572961-96x96.jpg","first_name":"Neha","user_url":"","last_name":"Singh","description":"I\u2019m a full-time freelance writer and editor who enjoys wordsmithing. The 8 years long journey as a content writer and editor has made me relaize the significance and power of choosing the right words. Prior to my writing journey, I was a trainer and human resource manager. WIth more than a decade long professional journey, I find myself more powerful as a wordsmith. As an avid writer, everything around me inspires me and pushes me to string words and ideas to create unique content; and when I\u2019m not writing and editing, I enjoy experimenting with my culinary skills, reading, gardening, and spending time with my adorable little mutt Neel."},{"term_id":2606,"user_id":40,"is_guest":0,"slug":"antaramandal","display_name":"Antara Mandal","avatar_url":"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2024\/07\/avatar_user_40_1721993829-96x96.jpeg","first_name":"Antara","user_url":"","last_name":"Mandal","description":"Antara Mandal as Analyst She graduated from Indian Institute of Technology Kanpur in 2024 and majored in electrical engineering. During her college years she tried to explore the data analytics field through courses offered by various online platforms like coursera, and found it interesting to learn and hence decided to pursue a career in this. Her hobbies are sketching, listening to music, watching movies sometimes and recently also started reading books related to fiction, adventure or mythology."}],"_links":{"self":[{"href":"https:\/\/www.pickl.ai\/blog\/wp-json\/wp\/v2\/posts\/20295","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.pickl.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.pickl.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.pickl.ai\/blog\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pickl.ai\/blog\/wp-json\/wp\/v2\/comments?post=20295"}],"version-history":[{"count":1,"href":"https:\/\/www.pickl.ai\/blog\/wp-json\/wp\/v2\/posts\/20295\/revisions"}],"predecessor-version":[{"id":20297,"href":"https:\/\/www.pickl.ai\/blog\/wp-json\/wp\/v2\/posts\/20295\/revisions\/20297"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.pickl.ai\/blog\/wp-json\/wp\/v2\/media\/20296"}],"wp:attachment":[{"href":"https:\/\/www.pickl.ai\/blog\/wp-json\/wp\/v2\/media?parent=20295"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.pickl.ai\/blog\/wp-json\/wp\/v2\/categories?post=20295"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.pickl.ai\/blog\/wp-json\/wp\/v2\/tags?post=20295"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.pickl.ai\/blog\/wp-json\/wp\/v2\/ppma_author?post=20295"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}