Introduction to Machine Learning with Gradient Boosting Models
This course aims to introduce you to building and understanding gradient boosting models through practical application in financial market predictions. It centers on using the Gradient Boosting Regressor to forecast price changes in Tesla stock, encompassing model training, hyperparameter tuning, and evaluation.
Lessons and practices
Lesson 1: Basic Gradient Boosting Model Training
Enhance Model by Adding the 'Close' Feature
Debug and Fix the Gradient Boosting Model
Fill in Missing Pieces for Model Training
Complete Gradient Boosting Model Implementation
Gradient Boosting Model Training with Tesla Data
Lesson 2: Evaluating Model with Cross-Validation
Changing the Number of Cross-Validation Folds
Evaluate Cross-Validation
Complete the Cross-Validation and Model Instantiation
Complete Cross-Validation Evaluation
Evaluating Gradient Boosting Model with Cross-Validation
Lesson 3: Hyperparameter Tuning Using GridSearchCV
Adjusting Cross-Validation Folds in GridSearchCV
Hyperparameter Tuning Debugging with GridSearchCV
Hyperparameter Tuning and Model Visualization
GridSearchCV for Hyperparameter Tuning
Hyperparameter Tuning with GridSearchCV
Lesson 4: Feature Importance in Gradient Boosting Models
Remove Volume Feature for Better Analysis
Feature Importance Calculation in Gradient Boosting Models
Calculate and Visualize Feature Importance in Gradient Boosting Model
Computing and Visualizing Feature Importance
Compute and Visualize Feature Importance in Gradient Boosting Model
Lesson 5: Using Early Stopping to Prevent Overfitting in Gradient Boosting Models
Apply Early Stopping Parameters to Model
Incorporate Early Stopping and Visualization in Gradient Boosting
Implementing a Gradient Boosting Regressor with Early Stopping
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