{"id":1421,"date":"2025-06-29T12:06:42","date_gmt":"2025-06-29T12:06:42","guid":{"rendered":"https:\/\/itxperts.co.in\/blog\/?p=1421"},"modified":"2025-08-02T09:29:30","modified_gmt":"2025-08-02T09:29:30","slug":"cbse-class-10-ai-unit-3-evaluating-models","status":"publish","type":"post","link":"https:\/\/itxperts.co.in\/blog\/cbse-class-10-ai-unit-3-evaluating-models\/","title":{"rendered":"Evaluating Models"},"content":{"rendered":"\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>A. Introduction to Model Evaluation<\/strong><\/h2>\n\n\n\n<p>After we create an AI model, we need to check if it is performing well and giving correct results. This process is called <strong>Model Evaluation<\/strong>.<\/p>\n\n\n\n<p>Without evaluation, we cannot trust the predictions made by the model.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>B. Why is Evaluation Important?<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>To check how accurate the model is.<\/li>\n\n\n\n<li>To identify errors or wrong predictions.<\/li>\n\n\n\n<li>To avoid using a model that gives biased or unfair results.<\/li>\n\n\n\n<li>To improve the model by finding where it performs poorly.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>C. Key Terms in Model Evaluation<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Term<\/th><th>Meaning<\/th><\/tr><\/thead><tbody><tr><td>Prediction<\/td><td>The output given by the AI model based on input data<\/td><\/tr><tr><td>Actual Value<\/td><td>The real or correct answer from the dataset<\/td><\/tr><tr><td>True Positive (TP)<\/td><td>Model predicted <strong>Yes<\/strong>, and it was <strong>Yes<\/strong><\/td><\/tr><tr><td>True Negative (TN)<\/td><td>Model predicted <strong>No<\/strong>, and it was <strong>No<\/strong><\/td><\/tr><tr><td>False Positive (FP)<\/td><td>Model predicted <strong>Yes<\/strong>, but it was <strong>No<\/strong> (wrongly positive)<\/td><\/tr><tr><td>False Negative (FN)<\/td><td>Model predicted <strong>No<\/strong>, but it was <strong>Yes<\/strong> (wrongly negative)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>D. Confusion Matrix<\/strong><\/h2>\n\n\n\n<p>A <strong>confusion matrix<\/strong> is a table used to describe the performance of a model on a set of test data.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Structure of Confusion Matrix<\/strong><\/h3>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th><\/th><th>Predicted: Yes<\/th><th>Predicted: No<\/th><\/tr><\/thead><tbody><tr><td><strong>Actual: Yes<\/strong><\/td><td>True Positive (TP)<\/td><td>False Negative (FN)<\/td><\/tr><tr><td><strong>Actual: No<\/strong><\/td><td>False Positive (FP)<\/td><td>True Negative (TN)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>This table helps us to calculate different performance metrics.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>E. Accuracy<\/strong><\/h2>\n\n\n\n<p><strong>Accuracy<\/strong> tells us the percentage of predictions that are correct.<\/p>\n\n\n\n<p><strong>Formula<\/strong>:<br>Accuracy=TP+TNTP+TN+FP+FN\\text{Accuracy} = \\frac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN\u200b<\/p>\n\n\n\n<p><strong>Example<\/strong>:<br>If TP = 50, TN = 30, FP = 10, FN = 10, then:<br>Accuracy=50+3050+30+10+10=80100=80%\\text{Accuracy} = \\frac{50 + 30}{50 + 30 + 10 + 10} = \\frac{80}{100} = 80\\%Accuracy=50+30+10+1050+30\u200b=10080\u200b=80%<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>F. Precision<\/strong><\/h2>\n\n\n\n<p><strong>Precision<\/strong> tells us how many of the predicted \u201cYes\u201d results were actually correct.<\/p>\n\n\n\n<p><strong>Formula<\/strong>:<br>Precision=TPTP+FP\\text{Precision} = \\frac{TP}{TP + FP}Precision=TP+FPTP\u200b<\/p>\n\n\n\n<p><strong>Example<\/strong>:<br>If TP = 50, FP = 10, then:<br>Precision=5050+10=5060\u224883.3%\\text{Precision} = \\frac{50}{50 + 10} = \\frac{50}{60} \\approx 83.3\\%Precision=50+1050\u200b=6050\u200b\u224883.3%<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>G. Recall (Sensitivity or True Positive Rate)<\/strong><\/h2>\n\n\n\n<p><strong>Recall<\/strong> tells us how many of the actual \u201cYes\u201d cases were correctly predicted by the model.<\/p>\n\n\n\n<p><strong>Formula<\/strong>:<br>Recall=TPTP+FN\\text{Recall} = \\frac{TP}{TP + FN}Recall=TP+FNTP\u200b<\/p>\n\n\n\n<p><strong>Example<\/strong>:<br>If TP = 50, FN = 10, then:<br>Recall=5050+10=5060\u224883.3%\\text{Recall} = \\frac{50}{50 + 10} = \\frac{50}{60} \\approx 83.3\\%Recall=50+1050\u200b=6050\u200b\u224883.3%<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>H. F1 Score<\/strong><\/h2>\n\n\n\n<p>The <strong>F1 Score<\/strong> combines both Precision and Recall into a single value using the harmonic mean.<\/p>\n\n\n\n<p><strong>Formula<\/strong>:<br>F1=2\u00d7Precision\u00d7RecallPrecision+RecallF1 = 2 \\times \\frac{Precision \\times Recall}{Precision + Recall}F1=2\u00d7Precision+RecallPrecision\u00d7Recall\u200b<\/p>\n\n\n\n<p>F1 Score is useful when we want to balance between Precision and Recall.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>I. Example Case Study \u2013 Spam Email Classifier<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Email<\/th><th>Actual<\/th><th>Predicted<\/th><\/tr><\/thead><tbody><tr><td>Email 1<\/td><td>Spam<\/td><td>Spam<\/td><\/tr><tr><td>Email 2<\/td><td>Not Spam<\/td><td>Spam<\/td><\/tr><tr><td>Email 3<\/td><td>Spam<\/td><td>Not Spam<\/td><\/tr><tr><td>Email 4<\/td><td>Not Spam<\/td><td>Not Spam<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>From this data:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>TP = 1 (Spam predicted as Spam)<\/li>\n\n\n\n<li>FP = 1 (Not Spam predicted as Spam)<\/li>\n\n\n\n<li>FN = 1 (Spam predicted as Not Spam)<\/li>\n\n\n\n<li>TN = 1 (Not Spam predicted as Not Spam)<\/li>\n<\/ul>\n\n\n\n<p>Now, calculate:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Accuracy = (TP + TN) \/ Total = (1 + 1) \/ 4 = 50%<\/li>\n\n\n\n<li>Precision = TP \/ (TP + FP) = 1 \/ (1 + 1) = 50%<\/li>\n\n\n\n<li>Recall = TP \/ (TP + FN) = 1 \/ (1 + 1) = 50%<\/li>\n<\/ul>\n\n\n\n<p>This shows the model is not very reliable.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>J. When to Use Which Metric<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Metric<\/th><th>Use When<\/th><\/tr><\/thead><tbody><tr><td>Accuracy<\/td><td>Data is balanced (equal Yes\/No outcomes)<\/td><\/tr><tr><td>Precision<\/td><td>You want to avoid false positives (e.g., fraud detection)<\/td><\/tr><tr><td>Recall<\/td><td>You want to avoid false negatives (e.g., disease detection)<\/td><\/tr><tr><td>F1 Score<\/td><td>You want a balance between precision and recall<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>K. Common Mistakes in Model Evaluation<\/strong><\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Only checking accuracy (not enough in real problems).<\/li>\n\n\n\n<li>Ignoring false positives and false negatives.<\/li>\n\n\n\n<li>Not checking for <strong>bias<\/strong> or <strong>fairness<\/strong> in predictions.<\/li>\n\n\n\n<li>Using small or unbalanced test data.<\/li>\n<\/ol>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>L. Activity Suggestion<\/strong><\/h2>\n\n\n\n<p>Give students a mini dataset of predicted and actual values. Ask them to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Build a confusion matrix.<\/li>\n\n\n\n<li>Calculate Accuracy, Precision, and Recall.<\/li>\n\n\n\n<li>Interpret what the values tell us about the model.<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>M. Keywords to Remember<\/strong><\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Term<\/th><th>Description<\/th><\/tr><\/thead><tbody><tr><td>Confusion Matrix<\/td><td>Table showing TP, TN, FP, FN<\/td><\/tr><tr><td>Accuracy<\/td><td>Percentage of correct predictions<\/td><\/tr><tr><td>Precision<\/td><td>Out of all predicted \u201cYes,\u201d how many were actually \u201cYes\u201d<\/td><\/tr><tr><td>Recall<\/td><td>Out of all actual \u201cYes,\u201d how many were predicted correctly<\/td><\/tr><tr><td>F1 Score<\/td><td>A single score combining precision and recall<\/td><\/tr><tr><td>TP, TN, FP, FN<\/td><td>Counts of different correct and incorrect predictions<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>N. Summary of the Unit<\/strong><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Evaluation is a key step to check how well an AI model performs.<\/li>\n\n\n\n<li>A <strong>confusion matrix<\/strong> helps organize the outcomes.<\/li>\n\n\n\n<li>Important metrics include <strong>Accuracy<\/strong>, <strong>Precision<\/strong>, <strong>Recall<\/strong>, and <strong>F1 Score<\/strong>.<\/li>\n\n\n\n<li>The choice of metric depends on the type of problem you are solving.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>A. Introduction to Model Evaluation After we create an AI model, we need to check if it is performing well and giving correct results. This process is called Model Evaluation. Without evaluation, we cannot trust the predictions made by the model. B. Why is Evaluation Important? C. Key Terms in Model Evaluation Term Meaning Prediction [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"googlesitekit_rrm_CAow44u0DA:productID":"","footnotes":""},"categories":[212],"tags":[201],"class_list":["post-1421","post","type-post","status-publish","format-standard","hentry","category-subject-specific-skills-class-10th","tag-aiclass-x"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Evaluating Models - Itxperts<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/itxperts.co.in\/blog\/cbse-class-10-ai-unit-3-evaluating-models\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Evaluating Models - Itxperts\" \/>\n<meta property=\"og:description\" content=\"A. 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