Implementation of Multiclass classification using CatBoost
Installing CatBoost
CatBoost module doesn’t come by default. We need to download it manually to our Python runtime environment.
Python3
!pip install catboost |
Importing required libraries
Now, we will import all necessary Python libraries required for this implementation like Pandas, NumPy, SKlearn, joblib, Seaborn and Matplotlib etc.
Python3
#importing Libraries import pandas as pd from catboost import CatBoostClassifier from sklearn.metrics import accuracy_score, classification_report from sklearn.model_selection import train_test_split import ipywidgets as widgets from IPython.display import display import joblib import numpy as np from sklearn.datasets import load_iris import matplotlib.pyplot as plt import seaborn as sns |
Loading dataset and data pre-processing
Python3
# Load the Iris dataset iris_df = load_iris() X = iris_df.data y = iris_df.target # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2 , random_state = 42 ) |
The Iris dataset is loaded, divided into training and testing sets using an 80/20 split, and the feature data is stored in X and the associated target labels are stored in Y. For reproducibility, the random seed is set to 42 and the dataset is taken from the load_iris function.
Multiclass classification using CatBoost
Multiclass or multinomial classification is a fundamental problem in machine learning where our goal is to classify instances into one of several classes or categories of the target feature. CatBoost is a powerful gradient-boosting algorithm that is well-suited and widely used for multiclass classification problems. In this article, we will discuss the step-by-step implementation of CatBoost for multiclass classification.
Table of Content
- What is Multiclass Classification
- What is CatBoost
- Implementation of Multiclass classification using CatBoost
- Exploratory Data Analysis
- Model Training and Evaluation
- Model Deployment