Train/Test Split Python

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There are 250 randomly generated data points that are obtained as follows:

[X, y] = getDataSet()  # getDataSet() randomly generates 250 data points

X looks like:

[array([[-2.44141527e-01, 8.39016956e-01],
        [ 1.37468561e+00, 4.97114860e-01],
        [ 3.08071887e-02, -2.03260255e-01],...

While y looks like:

y is array([[0.],
            [0.],
            [0.],...

(it also contains 1s)

So, I'm trying to split [X, y] into training and testing sets. The training set is suppose to be a random selection of 120 of the randomly generated data points. Here is how I'm generating the training set:

nTrain = 120

maxIndex = len(X)
randomTrainingSamples = np.random.choice(maxIndex, nTrain, replace=False)
trainX = X[randomTrainingSamples, :]  # training samples
trainY = y[randomTrainingSamples, :]  # labels of training samples    nTrain X 1

Now, what I can't seem to figure out is, how to get the testing set, which is the 130 other randomly generated data points that are not included in the training set:

testX =  # testing samples
testY =  # labels of testing samples nTest x 1

Suggestions are much appreciated. Thank you!


You can try this.

randomTestingSamples = [i for i in range(maxIndex) if i not in randomTrainingSamples]
testX =  X[randomTestingSamples, :]  # testing samples
testY =  y[randomTestingSamples, :]  # labels of testing samples nTest x 1

Train/Test Split and Cross Validation in Python, Hi everyone! After my last post on linear regression in Python, I thought it would only be natural to write a post about Train/Test Split and Cross  To split it, we do: x Train – x Test / y Train – y Test. That’s a simple formula, right? x Train and y Train become data for the machine learning, capable to create a model. Once the model is created, input x Test and the output should be equal to y Test. The more closely the model output is to y Test: the more accurate the model is.


You can use sklearn.model_selection.train_test_split:

import numpy as np
from sklearn.model_selection import train_test_split

X, y = np.ndarray((250, 2)), np.ndarray((250, 1))

trainX, testX, trainY, testY = train_test_split(X, y, test_size= 130)

trainX.shape
# (120, 2)
testX.shape
# (130, 2)
trainY.shape
# (120, 1)
testY.shape
# (130, 1)

3 Things You Need To Know Before You Train-Test Split, Know the dos and don'ts of train test splitting with scikit learn examples from real life use cases. Train/Test Split. Let’s see how to do this in Python. We’ll do this using the Scikit-Learn library and specifically the train_test_split method. We’ll start with importing the necessary libraries: import pandas as pd from sklearn import datasets, linear_model from sklearn.model_selection import train_test_split from matplotlib import pyplot as plt


You can shuffle the index and pick the first 120 as train and the next 130 as test

random_index = np.random.shuffle(np.arange(len(X)))
randomTrainingSamples = random_index[:120]
randomTestSamples = random_index[120:250]

trainX = X[randomTrainingSamples, :] 
trainY = y[randomTrainingSamples, :] 

testX = X[randomTestSamples, :]
testY = y[randomTestSamples, :]

A Guide on Splitting Datasets With Train_test_split Function, way to divide datasets into two parts: for testing and training with the Sklearn TL;DR – The train_test_split function is for splitting a single dataset for It is a Python library that offers various features for data processing that  What is train_test_split? train_test_split is a function in Sklearn model selection for splitting data arrays into two subsets: for training data and for testing data. With this function, you don't need to divide the dataset manually. By default, Sklearn train_test_split will make random partitions for the two subsets. However, you can also specify a random state for the operation.


Split Train Test, We usually split the data around 20%-80% between testing and training stages. Under supervised learning, we split a dataset into a training data and test data in​  Training and Test Data in Python Machine Learning As we work with datasets, a machine learning algorithmworks in two stages. We usually split the data around 20%-80% between testing and training


Train and Test Set in Python Machine Learning, But after training, we have to test the model on some test dataset. Using this we can easily split the dataset into the training and the testing datasets in various proportions. Train/Test Split and Cross Validation in Python. Training and Test Data in Python Machine Learning. As we work with datasets, a machine learning algorithmworks in two stages. We usually split the data around 20%-80% between testing and training stages. Under supervised learning, we split a dataset into a training data and test data in Python ML.


How to split your dataset to train and test datasets using SciKit Learn, How to split/partition a dataset into training and test datasets for, e.g., cross validation? python arrays optimization numpy. What is a good way to split a NumPy  Providing a value to random state will be helpful in reproducing the same values in the split when you re-run the program. If you don't provide any value to the random state, we will get different set of values for test and train after each run. In such a case, if you encounter any error, then it will not be helpful in debugging.