1- Supervised Learning:
Summary: Training models using labeled data to make predictions or decisions without explicit programming.- Definition: Supervised learning involves training a machine learning model on a labeled dataset, where the input data is paired with the correct output.
- Methods: Common techniques include regression and classification.
- Usage: Used for predictive modeling tasks such as forecasting sales, diagnosing diseases, and identifying objects in images.
- Advantages: High accuracy when sufficient labeled data is available, and the model can be validated against known outputs.
- Examples: Spam detection in emails, predicting house prices, and image recognition.
2- Unsupervised Learning:
Summary: Identifying patterns and relationships in data without labeled outcomes, such as clustering and association.- Definition: Unsupervised learning involves training a model on data without labeled responses, allowing the model to identify patterns and relationships in the data.
- Methods: Common techniques include clustering (e.g., K-means) and association (e.g., Apriori algorithm).
- Usage: Used for discovering hidden structures in data, such as customer segmentation, anomaly detection, and market basket analysis.
- Advantages: Can work with unstructured data and reveal insights that may not be immediately apparent.
- Examples: Grouping customers based on purchasing behavior, detecting fraudulent transactions, and recommending products.
3- Model Evaluation and Validation:
Summary: Assessing the performance of machine learning models using various metrics and techniques to ensure accuracy and reliability.- Definition: Model evaluation and validation involve techniques to assess the accuracy, reliability, and generalization ability of a machine learning model.
- Methods: Common metrics include accuracy, precision, recall, F1 score, and area under the ROC curve (AUC-ROC). Techniques include cross-validation and holdout validation.
- Usage: Ensures that the model performs well on new, unseen data and is not overfitting or underfitting.
- Advantages: Provides a robust measure of a model’s performance and helps in selecting the best model.
- Examples: Validating a classification model for cancer detection, evaluating a regression model for predicting stock prices, and tuning model hyperparameters for better performance.