Custom Machine Learning Model

This repository contains a custom implementation of a Decision Tree Classifier and Regressor in Python. The code contained in this repository is for self-practice purposes only. Please refrain from using these models for deployment. I recommend using scikit-learn's models for production use.

Contents

• Introduction
• Usage
• Example

Introduction

The models implemented here may not be optimal. The purpose i made this repository is because i am learning to build machine learning algorithms from scratch. If you like to try some models i have built, you can pull this repository and follow some example on how to use it.

Usage

Use this to create a machine learning model. Again, i recommend using scikit-learn's models for production use.

Example

If you want to use the custom DecisionTreeClassifier in your project or anything else, follow these steps:

1. Clone this repository to your workspace (if you have not done it)

   !git clone https://github.com/fadhilmuh/machine_learning_practice.git

2. Import the model you want. For example, DecisionTreeClassifier class:

   from machine_learning_practice.models.decision_trees import DecisionTreeClassifier

   or import the whole library

   import machine_learning_practice as MLP

3. initialize the DecisionTreeClassifier:

   tree = DecisionTreeClassifier()

   or if you use from the main library (following the previous step)

   tree = MLP.models.decision_trees.DecisionTreeClassifier()

4. fit the tree with your data

   tree.fit(features, target)

further example is available in the example directory.