The CatBoost algorithm is based on Gradient Descent which is a powerful technique for classification and regression problems in Machine Learning. In this article, I will introduce you to the CatBoost algorithm in Machine Learning and its implementation using Python.
CatBoost Algorithm in Machine Learning
The CatBoost algorithm is based on Gradient Descent and is a powerful technique for supervised machine learning tasks. It will be well suited to problems that involve categorical data. It is widely used for regression and classification tasks and it is also one of the most used algorithms in Kaggle competitions.
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The CatBoost algorithm is based on gradient decision trees and when training this model a set of decision trees is built consecutively. As training progresses, each successive tree is built with a reduced loss compared to the previous tree.
In the section below, I will take you through how to implement the CatBoost algorithm in Machine Learning by using the Python programming language.
CatBoost Algorithm using Python
Now let’s see how to implement the CatBoost algorithm in Machine Learning using Python. Here I will be using the classic Titanic dataset which is one of the most famous datasets in the data science community. Now let’s start by importing the necessary Python libraries and the dataset:
Before training the model, it is very important to prepare the data from the machine learning model, so here I will perform the below mentioned steps for data preparation:
- I will first remove the Survived column as that will be the target variable.
- Then I will split the data by creating two DataFrames like x and y, one will contain the target variable and the other will contain the useful features for the model.
- Next, I’ll convert the ‘Pclass’ column to a string data type, then fill in the null values present in the features.
So, as mentioned at the beginning, the CatBoost algorithm is a powerful machine learning algorithm for categorical features, here I will create two helper functions to generate a list of column indices containing the categorical data, then we need to convert all columns to category data type:
Now I’m going to split the data into 20% testing and 80% training:
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2, random_state=101, stratify=y)
Now before training the model let’s have a look at whether the data is properly balanced or not:
print('Test Survival Rate:',y_test.sum()/y_test.count())
Test Survival Rate: 0.3854748603351955
So we can see that the data is not balanced, there are so many ways to deal with it but I’m just going to downsample the data:
Final Step: Training Model
Now let’s train the model by using the CatBoost Algorithm using Python and print the classification report:
precision recall f1-score support 0 0.77 0.89 0.82 110 1 0.76 0.57 0.65 69 accuracy 0.77 179 macro avg 0.77 0.73 0.74 179 weighted avg 0.77 0.77 0.76 179
So this is how we can use the Catboost Algorithm in Machine Learning using Python. I hope you liked this article on CatBoost Algorithm in Machine Learning and its implementation using Python. Feel free to ask your valuable questions in the comments section below.