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Courses, in 8 stages

  1. 01Programming for AI/ML9 courses
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  7. 07Industry DS Projects15 courses
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  1. Courses
  2. PySpark for Data Science - V: ML Pipelines
  3. Build Decision Trees in PySpark

Evaluating the model on test data

Video · 3 min · Lesson 9 of 18

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PreviousBuilding the DecisionTreeClassifier modelNext lessonFeature Importance
MLOps and DeploymentPySpark for Data Science - V: ML Pipelines

    1. Reading Decision TreeFree7 min
    2. Download Resources1 min
    3. How Decision Tree Works?8 min
    1. Import required libraries and initialize SparkSession4 min
    2. Load the dataset2 min
    3. Prepare the data3 min
    4. Course Review1 min
    5. Building the DecisionTreeClassifier model4 min
    6. Evaluating the model on test data3 min
    7. Feature Importance1 min
    8. Improve the model (optional)1 min
    1. Creating a Pipeline & Hyperparameter Tuning12 min
    1. Random Forest Approach1 min
    2. Gradient Boosting1 min
    3. Compare results between 4 approaches3 min
    1. The problem with XGBoost for PySpark5 min
    2. Install XGBoost in PySpark2 min
    3. Run XGBoost in PySpark5 min

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