What is the train_test_split function and Example

Introduction

The train_test_split function takes in a dataset and a test size as parameters. The test size is a float between 0 and 1 that represents the proportion of the dataset that should be split into the test set. For example, if the test size is 0.2, then 20% of the data will be split into the test set.

 

 

Once the data is split into a training set and a test set, the machine learning model can be trained on the training set and evaluated on the test set. This evaluation is important because it allows us to gauge how well the model will perform on unseen data. A train_test_split function is a great tool for any machine learning workflow.

 

 

It’s important to remember, however, that the test size should be chosen carefully. If the test size is too small, then the model may not be able to properly evaluate the performance of the model. Conversely, if the test size is too large, then the model may not be able to properly learn from the training data.

 

 

 

What is the train_test_split function?

As the name suggests, train_test_split is a function that split your data into training and testing sets. It’s a common workflow in machine learning: you train your model on a training set, and then you evaluate your model on a testing set. The train_test_split function takes in your data and splits it into two sets: a training set and a testing set.

 

By default, the function will split your data 75%/25%, but you can also specify the proportions yourself. The function will also shuffle your data before splitting it, which is important if your data is ordered in some way (for example, if it’s sorted by label). Shuffling your data ensures that your training and testing sets are representative of the full dataset.

 

 

The train_test_split function takes two arguments: X and y. X is the data that you want to split, and y is the label for that data. You can split your data into a training set and a testing set with an 80/20 split like this:

 

 

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

 

 

This will give you a training set that consists of 80% of the data, and a testing set that consists of 20% of the data. You can also specify the random_state argument if you want to reproduce your results. Once you have your training and testing sets, you can train your model on the training set and use the testing set to evaluate how well your model performs.

 

 

 

Why use train_test_split? 

It’s important to use a train/test split (or some other form of cross-validation) because it gives us a way to measure our model’s performance on data that it hasn’t seen before. If we train our model on all of our data and then test it on the same data, we can get a misleading sense of how well our model is performing.

 

 

It’s possible that our model is overfitting to the training data and would not generalize well to new data. But if we split our data into training and testing sets, we can train our model on the training set and evaluate it on the testing set. This allows us to get a more accurate sense of how well our model is performing. 

 

 

 

Example of train_test_split Function

To use train-test split, you first need to split your data into a training set and a test set. This can be done using the train_test_split function from the sci-kit-learn library. train_test_split takes as input two arrays: the array of features (X) and the array of labels (y). It returns four arrays: X_train: the features of the training set y_train: the labels of the training set X_test: the features of the test set y_test: the labels of the test set

 

 

For example, let’s say you have a dataset with 100 observations, each with three features (X) and one label (y). You would first split the dataset into a training set and a test set using train_test_split: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) This will split the dataset into a training set with 80 observations and a test set with 20 observations. 

 

 

Let us consider a small example:

 

import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.arange(12).reshape((6, 2)), range(6)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)


print("X_train is",X_train)
print("X_test is",X_test)

print("y_train is",y_train)
print("y_test is",y_test)

Output

 

 

 

Also, read about FuzzyWuzzy Library in Python.