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6.3.2 Random Forest Regression -
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Classroom Contents
Machine Learning for Beginners - Data Scientists and Analysts
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- 1 1.3.0 Machine Learning in 4 Lines of Code -
- 2 2.0.0 Machine Learning Basics -
- 3 3.0.0 Machine Learning in Business -
- 4 3.1.0 How to know when to use ML -
- 5 3.2.0 Ethics in Machine Learning -
- 6 4.1.0 Holistically Designing A ML Algorithm Using CRISP-DM -
- 7 4.2.0 Business Understanding and Data Understanding -
- 8 4.3.0 Data Preparation -
- 9 4.4.0 Modeling -
- 10 4.4.1 Determining Which Model to Use -
- 11 4.4.2 Implementing a Model -
- 12 4.5.0 Evaluation -
- 13 5.0.0 Data Cleaning and Environment Setup -
- 14 5.1.0 Setting up and Environment -
- 15 5.2.0 Data Cleaning Techniques -
- 16 5.2.2 Basic Data Format -
- 17 5.2.3 Remove Columns with One Unique Value -
- 18 5.2.4 Data Types -
- 19 5.2.5 Parsing Dates -
- 20 5.2.6 Missing Data -
- 21 5.2.7 Select Target Column -
- 22 5.2.8 Data Encoding -
- 23 5.2.9 Multicollinearity -
- 24 5.2.10 Feature Engineering -
- 25 5.2.11 Scaling -
- 26 5.2.12 Train Test Split -
- 27 6.0.0 Regression -
- 28 6.1.0 Data Cleaning: Regression -
- 29 6.2.0 Model Selection: Regression -
- 30 6.3.1 Linear Regression -
- 31 6.3.2 Random Forest Regression -
- 32 6.3.3 XGBoost Regression -
- 33 6.4.0 Hyperparameter Tuning -
- 34 7.0.0 Classification Practice -
- 35 7.2.1 Logistic Regression -
- 36 7.2.2 Random Forest Classifier -
- 37 7.2.3 LightGBM -
- 38 7.3.0 Model Evaluation: Classification -
- 39 7.3.1 Confusion Matrix -
- 40 7.3.2 Area Under the Curve AUC -
- 41 7.3.3 F1 Score -
