𝐂𝐡𝐞𝐜𝐤 𝐘𝐨𝐮𝐫 𝐌𝐨𝐝𝐞𝐥: 𝐈𝐬 𝐈𝐭 𝐆𝐞𝐭𝐭𝐢𝐧𝐠 𝐈𝐭 𝐑𝐢𝐠𝐡𝐭, 𝐨𝐫 𝐉𝐮𝐬𝐭 𝐅𝐚𝐤𝐢𝐧𝐠 𝐈𝐭?⁣

Your models pose, my models predict. We are not the same😤

Training a machine learning model is like teaching a pet — you want it to learn the right tricks without overdoing it or forgetting the basics! But how do you know if your model is truly learning meaningful patterns or just memorizing noise in the data?

Techniques like 𝐜𝐫𝐨𝐬𝐬-𝐯𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧, 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐜𝐮𝐫𝐯𝐞𝐬, 𝐚𝐧𝐝 𝐚𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧𝐬 help you quickly spot whether your model is 𝐮𝐧𝐝𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠, 𝐨𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠, 𝐨𝐫 𝐩𝐞𝐫𝐟𝐞𝐜𝐭𝐥𝐲 𝐛𝐚𝐥𝐚𝐧𝐜𝐞𝐝. Let’s dive in and find out.⁣

𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐭𝐨 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐔𝐧𝐝𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠⁣:

𝟏. 𝐂𝐫𝐨𝐬𝐬-𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧⁣

𝐇𝐨𝐰 𝐈𝐭 𝗪𝐨𝐫𝐤𝐬:

Split your data into multiple folds, and train and evaluate the model on each fold.⁣

𝗪𝐡𝐚𝐭 𝐭𝐨 𝐋𝐨𝐨𝐤 𝐅𝐨𝐫:⁣

Low performance across all folds → 𝐔𝐧𝐝𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠.⁣

High variance between folds → 𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠.⁣

𝐏𝐫𝐨 𝐓𝐢𝐩: Use stratified k-fold for imbalanced datasets to ensure each fold represents the class distribution.⁣

𝟐. 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐂𝐮𝐫𝐯𝐞𝐬⁣

𝐇𝐨𝐰 𝐈𝐭 𝗪𝐨𝐫𝐤𝐬:

Plot the model’s performance (𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲↑ or 𝐞𝐫𝐫𝐨𝐫-𝐥𝐨𝐬𝐬↓) on both training and validation sets over time or as the training set size increases.⁣

𝗪𝐡𝐚𝐭 𝐭𝐨 𝐋𝐨𝐨𝐤 𝐅𝐨𝐫:⁣

Training and validation performance stabilize at a low level →𝐔𝐧𝐝𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠.⁣

Large gap between training and validation performance → 𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠.⁣

𝟑. 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐢𝐧𝐠 𝐀𝐜𝐭𝐢𝐯𝐚𝐭𝐢𝐨𝐧𝐬 (𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬)⁣

𝐇𝐨𝐰 𝐈𝐭 𝗪𝐨𝐫𝐤𝐬:

Analyze the activations of layers in a neural network to see if the model is learning useful features or overfitting to noise.⁣

𝐓𝐨𝐨𝐥𝐬: TensorBoard, Grad-CAM, or activation heatmaps.⁣

𝗪𝐡𝐚𝐭 𝐭𝐨 𝐋𝐨𝐨𝐤 𝐅𝐨𝐫:⁣

Uniform or uninformative activations → 𝐔𝐧𝐝𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠.⁣

Overly specific activations (memorizing noise) → 𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠.⁣⁣

𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠: 𝐒𝐲𝐦𝐩𝐭𝐨𝐦𝐬 𝐚𝐧𝐝 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬⁣

𝐒𝐲𝐦𝐩𝐭𝐨𝐦𝐬:⁣

1.Model performs well on training data but poorly on validation/test data.⁣

2.High variance in cross-validation results.⁣

Overfitted bed: It fits the model perfectly… but good luck getting anyone else in!

𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬:⁣

1.𝐑𝐞𝐝𝐮𝐜𝐞 𝐌𝐨𝐝𝐞𝐥 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲:

Use fewer layers (neural networks) or fewer parameters (reduce tree depth in decision trees).⁣

2.𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐓𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬:⁣

𝐋𝟏 (𝐋𝐚𝐬𝐬𝐨): Encourages sparsity and feature selection.⁣

𝐋𝟐 (𝐑𝐢𝐝𝐠𝐞): Smooths weights to prevent over-reliance on specific features.⁣

𝐃𝐫𝐨𝐩𝐨𝐮𝐭: Randomly drop units during training (neural networks).⁣

3.𝐄𝐚𝐫𝐥𝐲 𝐒𝐭𝐨𝐩𝐩𝐢𝐧𝐠: Stop training when validation performance stops improving.⁣

4.𝐃𝐚𝐭𝐚 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: Increase dataset size through transformations⁣

5.𝐁𝐚𝐭𝐜𝐡 𝐍𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Normalize activations to stabilize training (neural networks).⁣

6.𝐔𝐬𝐞 𝐒𝐢𝐦𝐩𝐥𝐞𝐫 𝐌𝐨𝐝𝐞𝐥𝐬: Switch to a less complex algorithm.⁣⁣

𝐔𝐧𝐝𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠: 𝐒𝐲𝐦𝐩𝐭𝐨𝐦𝐬 𝐚𝐧𝐝 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬⁣

𝐒𝐲𝐦𝐩𝐭𝐨𝐦𝐬:⁣

Model performs poorly on both training and validation/test data.⁣

Low performance across all cross-validation folds.⁣

You are underfit for this king size bed, get a wife and have children

𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬:⁣

1.𝐈𝐧𝐜𝐫𝐞𝐚𝐬𝐞 𝐌𝐨𝐝𝐞𝐥 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲: Add more layers (neural networks) or increase the number of parameters (increase tree depth in decision trees).⁣

2.𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: Create new features or transform existing ones (polynomial features, interaction terms).⁣

3.𝐌𝐨𝐫𝐞 𝐃𝐚𝐭𝐚: Increase the number of data in the dataset.⁣

4.𝐇𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐓𝐮𝐧𝐢𝐧𝐠: Adjust hyperparameters like learning rate, number of layers, or number of estimators.⁣

5.𝐄𝐧𝐬𝐞𝐦𝐛𝐥𝐞 𝐌𝐞𝐭𝐡𝐨𝐝𝐬: Combine multiple models (bagging, boosting) to improve accuracy and handle complex relationships.⁣⁣

𝐂𝐨𝐦𝐦𝐨𝐧 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐁𝐨𝐭𝐡 𝐔𝐧𝐝𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠⁣:

1.𝐂𝐥𝐞𝐚𝐧 𝐃𝐚𝐭𝐚: Ensure the data is properly preprocessed, free from outliers, and representative of the problem space.⁣

2.𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬: Choose or modify loss functions to better suit the problem (focal loss for class imbalance, Huber loss for robust regression).⁣

3.𝐂𝐡𝐚𝐧𝐠𝐞 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬: Experiment with different algorithms that might perform better for your specific problem.⁣

⁣

𝐁𝐢𝐚𝐬-𝐕𝐚𝐫𝐢𝐚𝐧𝐜𝐞 𝐓𝐫𝐚𝐝𝐞𝐨𝐟𝐟: 𝐓𝐡𝐞 𝐁𝐢𝐠 𝐏𝐢𝐜𝐭𝐮𝐫𝐞⁣

High Bias = You are bias towards someone, not neutral! and High Variance = More scattered!

𝐔𝐧𝐝𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠 → 𝐇𝐢𝐠𝐡 𝐁𝐢𝐚𝐬: The model is too simple to capture the underlying patterns.⁣

𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠 → 𝐇𝐢𝐠𝐡 𝐕𝐚𝐫𝐢𝐚𝐧𝐜𝐞: The model is too complex and memorizes noise instead of learning generalizable patterns.⁣

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