Your model is too good<\/strong> at remembering the training data but bad<\/strong> at handling new\/unseen data.<\/p>\n\n\n\n \ud83d\udcda Trained too well on homework Here are the top solutions<\/strong> \u2014 simple and powerful! \ud83d\udcaa<\/p>\n\n\n\n If your model is too big (too many layers\/neurons\/trees), it’s easy to overfit.<\/p>\n\n\n\n \u2705 Try a smaller neural network<\/strong> \ud83e\udde0 Simpler model = better generalization<\/p>\n\n\n\n This helps your model avoid memorizing too much.<\/p>\n\n\n\n \u2705 For neural networks:<\/p>\n\n\n\n \u2705 For linear models:<\/p>\n\n\n\n More training data = better generalization! \ud83d\udcca\ud83d\udcc8<\/p>\n\n\n\n \u2705 Try to:<\/p>\n\n\n\n \ud83c\udd95 New examples help reduce overfitting!<\/p>\n\n\n\n Watch your validation loss<\/strong> \ud83d\udc40<\/p>\n\n\n\n \ud83d\udcc9 When validation loss starts going up<\/strong>, stop training! Instead of just one validation set, use k-fold cross-validation<\/strong> to check performance more fairly \ud83d\udca1<\/p>\n\n\n\n \ud83d\udd01 It splits your data into multiple sets and tests on each one<\/p>\n\n\n\n Too many epochs? Your model might memorize!<\/p>\n\n\n\n \u23f1\ufe0f Try reducing the number of training epochs<\/strong><\/p>\n\n\n\n This makes training harder and helps prevent memorizing.<\/p>\n\n\n\n \u2705 Add a bit of random noise<\/strong> to input data <\/p>\n","protected":false},"excerpt":{"rendered":"
\u274c Fails on the test<\/p>\n\n\n\n
\n\n\n\n\ud83d\udee0\ufe0f What To Do When Your Model is Overfitted?<\/h2>\n\n\n\n
\n\n\n\n1. \u2702\ufe0f Use Less Complex Model<\/strong><\/h3>\n\n\n\n
\u2705 Reduce depth<\/strong> in decision trees or random forest<\/p>\n\n\n\n
\n\n\n\n2. \ud83e\uddfc Add Regularization<\/strong><\/h3>\n\n\n\n
\n
\n
\n\n\n\n3. \ud83d\udd04 Use More Data<\/strong><\/h3>\n\n\n\n
\n
\n\n\n\n4. \ud83e\uddea Use Early Stopping<\/strong><\/h3>\n\n\n\n
\u2705 This saves your model from over-training<\/p>\n\n\n\n
\n\n\n\n5. \ud83d\udcca Cross-Validation<\/strong><\/h3>\n\n\n\n
\n\n\n\n6. \ud83e\udde0 Reduce Training Time<\/strong><\/h3>\n\n\n\n
\n\n\n\n7. \ud83c\udf2a\ufe0f Add Noise to Data<\/strong><\/h3>\n\n\n\n
\u2705 In text: change word order, add typos, etc.<\/p>\n\n\n\n
\n\n\n\n\u2705 Summary Cheat Sheet:<\/h2>\n\n\n\n
Fix<\/th> What It Does<\/th> Emoji<\/th><\/tr><\/thead> \u2702\ufe0f Simpler Model<\/td> Prevents memorization<\/td> \ud83e\udd13<\/td><\/tr> \ud83e\uddfc Regularization<\/td> Adds penalty to over-complex models<\/td> \ud83e\uddfd<\/td><\/tr> \ud83d\udd04 More Data<\/td> Helps model generalize better<\/td> \ud83d\udcc8<\/td><\/tr> \u23f9\ufe0f Early Stopping<\/td> Stops training at the right time<\/td> \u23f1\ufe0f<\/td><\/tr> \ud83d\udcca Cross-Validation<\/td> Ensures stable performance<\/td> \ud83d\udd01<\/td><\/tr> \ud83c\udf2a\ufe0f Add Noise \/ Augmentation<\/td> Makes learning more robust<\/td> \ud83c\udfad<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\n\n\n\n