Components of Machine Learning

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Evaluation Metrics :​

• Metrics are used to evaluate the performance of the model on test data. Different metrics are used depending on the type of task (classification, regression, etc.).
• Common Evaluation Metrics :
  • Accuracy (for classification tasks)
  • Precision, Recall, F1-Score (for imbalanced classification tasks)
  • Mean Absolute Error (MAE) , Root Mean Squared Error (RMSE) (for regression)
  • Confusion Matrix (for classification)
  • ROC Curve and AUC (for binary classification)

Hyperparameters :​

• Hyperparameters are settings that control the learning process but are not learned from the data. These are set before training begins and can influence the model's performance significantly.
• Examples of Hyperparameters :
  • Learning rate : Controls how quickly the model updates its parameters.
  • Number of layers in a neural network.
  • Tree depth in decision trees.

Model Validation and Cross-Validation :​

• Validation is the process of tuning the model by adjusting hyperparameters using a validation dataset. This helps prevent overfitting (where the model performs well on training data but poorly on new data).
• Cross-Validation : A method of evaluating model performance by splitting the data into multiple folds and training/testing the model on different combinations of these folds (eg, k-fold cross-validation).

Overfitting and Underfitting :​

• Overfitting occurs when the model performs well on training data but poorly on unseen data, because it has learned the noise or specific details of the training set.
• Underfitting occurs when the model is too simple to capture the underlying patterns in the data, leading to poor performance on both training and test data.

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