Hands-on Machine Learning in Python — Decision Tree Classification

Hands-on Machine Learning in Python — Decision Tree Classification

Hands-on Machine Learning in Python — Decision Tree Classification. Today, in this article, I am going to show you the entire workflow of implementing a Decision Tree Classification Model.

As one of the most popular classic machine learning algorithms, the Decision Tree is much more intuitive than the others for its explainability. Today, in this article, I am going to show you the entire workflow of implementing a Decision Tree Classification Model.

A typical workflow of a machine learning task usually starts from the data wrangling, since the data we have got initially often cannot be directly used. That is so-called the raw data. The typical workflow is usually as follows.

  1. Problem Statement
  2. Exploratory Data Analysis (EDA)
  3. Data Cleansing
  4. Feature Engineering
  5. Model Training
  6. Model Evaluation

Please be noticed that the workflow might not be linear. For example, sometimes the performance of the model is not quite well after we have done the model evaluation, and we still have other ideas to try. Then, we might go back to either the step among 3–5 to apply our ideas that might improve the model.

In this article, I’ll show these steps as linear. I assume that you have already known what is a decision tree and roughly how it works. However, if not, I have written many articles to introduce the decision tree model with intuitions. Please check them out at the end of this article if you need to.

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