Introduction to Machine Learning and Linear Regression

Introduction to Machine Learning

• Machine learning automates analytical model building.
• Algorithms learn from data without explicit programming.
• Used for prediction, classification, and pattern recognition.
• Types include supervised, unsupervised, and reinforcement learning.

Linear Regression 1

• Predicts a continuous target variable using linear relationships.
• Uses ordinary least squares (OLS) to minimize error.
• Assumes linear relationship between variables.
• Simple to implement and interpret.

Linear Regression 2

• Handles multiple predictor variables (multiple linear regression).
• Coefficient interpretation shows variable impact.
• Sensitive to outliers and multicollinearity.
• Model assumptions need checking (normality, homoscedasticity).

Linear Regression 3

• Model evaluation: R-squared, RMSE, MAE measures performance.
• Feature scaling improves model training.
• Regularization techniques (Ridge, Lasso) address overfitting.
• Model selection uses techniques like cross-validation.

Polyregression, Bias-Variance

• Polynomial regression models non-linear relationships.
• Higher-degree polynomials can overfit.
• Bias is the error from simplifying assumptions.
• Variance is the error from model sensitivity to training data.
• Bias-variance trade-off aims to find optimal model complexity.

Logistic Regression

• Predicts a categorical target variable (binary or multinomial).
• Uses a sigmoid function to produce probabilities.
• Interprets coefficients similarly to linear regression, but for log-odds.
• Evaluation metrics include accuracy, precision, recall, and F1-score.

Metrics and Imbalanced Data

• Imbalanced datasets have disproportionate class representation.
• Standard accuracy can be misleading for imbalanced data.
• Precision, recall, and F1-score provide nuanced evaluation.
• Techniques like oversampling, undersampling, and cost-sensitive learning address imbalance.

Metrics - K-Fold

• K-fold cross-validation improves model evaluation robustness.
• Data is divided into k folds; each fold is used once for testing.
• Averaged performance provides a more stable estimate.
• Reduces bias compared to train-test split.

KNN (K-Nearest Neighbors)

• Non-parametric, instance-based learning algorithm.
• Classifies data points based on majority class among k-nearest neighbors.
• Distance metrics (Euclidean, Manhattan) determine nearest neighbors.
• Sensitive to scale of features; requires feature scaling.
• Computationally expensive for large datasets.

Decision Tree

• Creates a tree-like model for classification or regression.
• Uses recursive partitioning to split data based on features.
• Interpretable, easy to visualize.
• Prone to overfitting; pruning or ensemble methods mitigate this.

Bagging and Random Forest

• Bagging creates multiple models from bootstrapped samples.
• Random Forest combines bagging with random feature selection.
• Reduces variance, improves accuracy and robustness.
• Less prone to overfitting than individual decision trees.

Boosting, Stacking, Etc.

• Boosting sequentially builds models, weighting misclassified instances.
• Examples include AdaBoost, Gradient Boosting Machines (GBM), XGBoost.
• Stacking combines predictions from multiple base models.
• Advanced ensemble methods improve predictive performance.

Naive Bayes

• Based on Bayes' theorem, assuming feature independence.
• Simple, efficient, and effective in many applications (text classification).
• Probability-based classification.
• Feature independence assumption might be violated in real-world scenarios.

Support Vector Machine (SVM)

• Finds optimal hyperplane to separate data points.
• Effective for high-dimensional data.
• Utilizes kernel functions for non-linear separation.
• Tuning parameters (kernel type, regularization) crucial for performance.

This quiz covers the fundamentals of machine learning and dives deep into linear regression techniques, including simple and multiple regression. You'll explore concepts such as model evaluation, coefficient interpretation, and the impact of outliers. Test your understanding of these essential topics that are vital for data analysis and predictive modeling.