Hyperparameter Tuning in Tree-Based Models

This course is designed  to help the learners understand various hyperparameters used in tree-based models such as decision tree to solve regression and classification problems and its implementation in real-world business cases.

  • icon-videos 2 Videos
  • icon-data 6 Data Cases
  • icon-reading 2 Readings
  • icon-quiz 4 Quizzes
Hyperparameter Tuning in Tree-Based Models
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    Difficulty: Advanced

    Prior knowledge or professional experience is strongly recommended.

  • Asset 1
    Duration: Approximately 12 hours

Course Overview

  • Tree-based models are one of the most widely used algorithms in the realm of machine learning and data science.
  • Tree-based models have multiple hyperparameters and choosing their values optimally can lead to significant improvement in model performance.
  • Learn the working of the decision tree algorithm and how different hyperparameters can be implemented to boost model performance.
  • Gain practical experience by applying the understanding in 4 real-world business case scenarios.

What’s included

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Lifetime Access

Access this project for life once completed

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Flexible Scheduling

Start learning online immediately, at your own pace

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Desktop Only

We recommend completing this project on a desktop

Skills You Will Learn

Machine Learning

Exploratory Data Analysis

Model Building

Hyperparameter Tuning

Model Building

Model Evaluation and Validation

Decision Tree Classifier & Regressor

Ensemble Algorithms

Associated Learning Tracks

Syllabus

How it Works

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