Essential Machine Learning Algorithms and Metrics

Evaluation Metrics for ML Models

• Accuracy: The ratio of correctly predicted instances.

• Precision: Correct positive predictions divided by total predicted positives.

• Recall: Correct positive predictions divided by actual positives.

• F1 Score: The harmonic mean of precision and recall.

  

K-Nearest Neighbors (KNN) Algorithm

• A classification algorithm that works by finding the 'k' closest training examples to a data point.

• Strengths: Simple to understand, effective for smaller datasets.

• Weaknesses: Sensitive to irrelevant features and the scale of the data.

• Applications: Image recognition, recommendation systems.

Ensemble Learning Techniques

• Combines multiple models to improve predictive performance.

• Methods:

  • Bagging (e.g., Random Forests)
  • Boosting (e.g., AdaBoost)

• Advantages: Increases accuracy and reduces overfitting.

• Use Cases: Forecasting, fraud detection.

Decision Tree Model

• A model that splits data into branches to make decisions.

• Key Features: Easy to visualize and highly interpretable.

• Limitations: Prone to overfitting.

• Examples: Classifying customer behavior, credit scoring.

Linear Regression

• Predicts a target variable by fitting a linear equation to observed data.

• Formula: Y = β0 + β1X1 + ... + βnXn

• Use Cases: Sales forecasting, risk assessment.

Logistic Regression

• Used for binary classification; predicts the probability of an event occurring.

• Formula: P = 1 / (1 + e^(-Z)), where Z is the linear combination of inputs.

• Applications: Credit scoring, marketing response prediction.

Bayesian Networks

• Graphical models that represent probabilistic relationships among variables.

• Key Features: Incorporates prior knowledge and evidence.

• Applications: Diagnosing diseases, risk management.

Support Vector Machines (SVM)

• A supervised learning model that finds the best hyperplane for classification.

• Key Features: Effective in high-dimensional spaces, robust against overfitting.

• Use Cases: Text categorization, image classification.

Key Machine Learning Terminology

• Overfitting: Occurs when the model learns noise instead of general patterns.

• Training Set: The dataset used to train the model.

• Test Set: The dataset used to evaluate the model’s performance.