Home achine Learning Implementation of Multiclass classification using CatBoost

Implementation of Multiclass classification using CatBoost

What is CatBoost

Exploratory Data Analysis

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.

																																						

																								

																										




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#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

																																						

																								

																										




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# 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
    • 




																																						

																							

										


										
										

											
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														What is Multiclass Classification

													

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														What is CatBoost

													

														Multiclass classification is a kind of supervised learning task in machine learning that involves classifying instances into one of three or more distinct classes or categories of the target feature. For multiclass classification, the minimum number of classes of the target feature should be three. In Binary classification tasks, our goal is to classify instances into one of two classes (like fraud detected or no fraud). Still, multiclass classification is used for problems where multiple possible outcomes are there. Multiclass classification can be used in various fields of Natural Language Processing tasks like Sentiment analysis(positive, negative, or neutral), text categorization language identification, etc. Also in speech recognition and recommendation systems(like recommending products, movies, or any content to users based on their preferences and behavior) multiclass classification is used....
													

												
											

																						

												
													

														Implementation of Multiclass classification using CatBoost

													

														CatBoost or Categorical Boosting is a machine learning algorithm developed by Yandex, a Russian multinational IT company. This special boosting algorithm is based on the gradient boosting framework and is designed to handle categorical features more effectively than traditional gradient boosting algorithms. CatBoost incorporates techniques like ordered boosting, oblivious trees, and advanced handling of categorical variables to achieve high performance with minimal hyperparameter tuning. CatBoost classifier is an implementation of the CatBoost algorithm which is specifically used for classification task. CatBoost is very efficient for multiclass classification problems. Next we will see a step-by-step guide for implementation of CatBoost classifier for Multiclass classification using Iris dataset. Then a detailed Exploratory Data Analysis is given for understanding dataset. After that, we will develop a user interface where this model will run in background and users can give input and get real-time predicted results....
													

												
											

																						

												
													

														Exploratory Data Analysis

													

														Installing CatBoost...
													

												
											

																						

												
													

														Model Training and Evaluation

													

														...
													

												
											

																						

												
													

														Model Deployment

													

														...
													

												
											

																						

												
													

														Conclusion

													

														...
													

												
											

																					


									

									

										
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