Classification metrics and evaluation techniques in supervised learning.

Train-Test-Split Technique

• The dataset is divided into a training set (70-80% of data) for model training and a test set for evaluating model performance on unseen data.
• This technique helps estimate how well machine learning algorithms can predict outcomes.

Key Evaluation Metrics

• Accuracy: The ratio of correctly predicted instances to the total instances in the dataset.
• Confusion Matrix: A table that shows the breakdown of true positives, true negatives, false positives, and false negatives.

Precision, Recall, and F1 Score

• Precision: The fraction of true positives among all predicted positives, important in scenarios like movie recommendations.
• Recall: The fraction of true positives among all actual positives, crucial in fields like medicine to avoid false negatives.
• F1 Score: The harmonic mean of precision and recall, useful when both metrics are equally important.

Regression Model Evaluation

It is important to understand how accurately these models can predict continuous numerical values. Regression models often make errors.

• Evaluating regression models involves measuring prediction errors, which are the differences between actual values and predicted values.
• The error of a model is quantified through various regression metrics that provide insights into its performance.

Key Regression Metrics

• Mean Absolute Error (MAE) measures the average absolute difference between predicted and actual values.
• Mean Squared Error (MSE) calculates the average of the squared differences, while Root Mean Squared Error (RMSE) is the square root of MSE, making it easier to interpret.
• R-squared is the amount of variance in the dependent variable that the independent variable can explain. It is also called the coefficient of determination and measures the model's goodness of fit.

Understanding R-squared

• R-squared indicates the proportion of variance in the dependent variable that can be explained by the independent variable, with values ranging from 0 (poor fit) to 1 (perfect fit).
• It is essential to visualize results and consider multiple metrics for a comprehensive evaluation of model performance.

Unsupervised Learning Models: Heuristics and Techniques

Evaluation Challenges

• Unsupervised learning lacks predefined labels and are therefore often subjective, making it difficult to assess model quality.
• Stability is crucial; models should perform consistently across different data subsets.
• There is no one-size-fits-all approach to evaluating unsupervised learning models, and a combinationn of methods is essential.

Heuristics for Cluster Quality

• Internal metrics (e.g., silhouette score, Davies-Bouldin index) assess clustering based on input data.
• External metrics (e.g., adjusted Rand index, normalized mutual information) compare clustering results with known labels.

Dimensionality Reduction Evaluation

• Techniques like PCA and t-SNE are used to visualize data while retaining important information.
• Metrics such as explained variance ratio and reconstruction error help evaluate how well reduced data preserves original relationships.

Python Classification Metrics and Evaluation
Python Evaluating Random Forest Performance
Python evaluating k-means clustering