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    which of the following sklearn method is used to split the data into training and testing

    Mohammed

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    get which of the following sklearn method is used to split the data into training and testing from screen.

    sklearn.model_selection.train_test_split — scikit

    Examples using sklearn.model_selection.train_test_split: Release Highlights for scikit-learn 0.24 Release Highlights for scikit-learn 0.24 Release Highlights for scikit-learn 0.23 Release Highlight...

    sklearn.model_selection.train_test_split

    sklearn.model_selection.train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)

    [source]

    Split arrays or matrices into random train and test subsets.

    Quick utility that wraps input validation and next(ShuffleSplit().split(X, y)) and application to input data into a single call for splitting (and optionally subsampling) data in a oneliner.

    Read more in the User Guide.

    Parameters:

    *arrayssequence of indexables with same length / shape[0]

    Allowed inputs are lists, numpy arrays, scipy-sparse matrices or pandas dataframes.

    test_sizefloat or int, default=None

    If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. If train_size is also None, it will be set to 0.25.

    train_sizefloat or int, default=None

    If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.

    random_stateint, RandomState instance or None, default=None

    Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls. See Glossary.

    shufflebool, default=True

    Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.

    stratifyarray-like, default=None

    If not None, data is split in a stratified fashion, using this as the class labels. Read more in the User Guide.

    Returns:

    splittinglist, length=2 * len(arrays)

    List containing train-test split of inputs.

    New in version 0.16: If the input is sparse, the output will be a scipy.sparse.csr_matrix. Else, output type is the same as the input type.

    Examples >>>

    >>> import numpy as np

    >>> from sklearn.model_selection import train_test_split

    >>> X, y = np.arange(10).reshape((5, 2)), range(5)

    >>> X array([[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]) >>> list(y) [0, 1, 2, 3, 4] >>>

    >>> X_train, X_test, y_train, y_test = train_test_split(

    ... X, y, test_size=0.33, random_state=42)

    ... >>> X_train array([[4, 5], [0, 1], [6, 7]]) >>> y_train [2, 0, 3] >>> X_test array([[2, 3], [8, 9]]) >>> y_test [1, 4] >>>

    >>> train_test_split(y, shuffle=False)

    [[0, 1, 2], [3, 4]]

    Examples using sklearn.model_selection.train_test_split

    Release Highlights for scikit-learn 0.24

    Release Highlights for scikit-learn 0.23

    Release Highlights for scikit-learn 0.22

    Comparison of Calibration of Classifiers

    Probability Calibration curves

    Probability calibration of classifiers

    Classifier comparison

    Recognizing hand-written digits

    Principal Component Regression vs Partial Least Squares Regression

    Post pruning decision trees with cost complexity pruning

    Understanding the decision tree structure

    Kernel PCA

    Comparing random forests and the multi-output meta estimator

    स्रोत : scikit-learn.org

    Split Your Dataset With scikit

    In this tutorial, you'll learn why it's important to split your dataset in supervised machine learning and how to do that with train_test_split() from scikit-learn.

    Split Your Dataset With scikit-learn's train_test_split()

    by Mirko Stojiljković 3 Comments data-science intermediate machine-learning

    Tweet Share Email Table of Contents

    The Importance of Data Splitting

    Training, Validation, and Test Sets

    Underfitting and Overfitting

    Prerequisites for Using train_test_split()

    Application of train_test_split()

    Supervised Machine Learning With train_test_split()

    Minimalist Example of Linear Regression

    Regression Example

    Classification Example

    Other Validation Functionalities

    Conclusion Remove ads

    Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Splitting Datasets With scikit-learn and train_test_split()

    One of the key aspects of supervised machine learning is model evaluation and validation. When you evaluate the predictive performance of your model, it’s essential that the process be unbiased. Using train_test_split() from the data science library scikit-learn, you can split your dataset into subsets that minimize the potential for bias in your evaluation and validation process.

    In this tutorial, you’ll learn:

    Why you need to split your dataset in supervised machine learning

    Which subsets of the dataset you need for an unbiased evaluation of your model

    How to use train_test_split() to split your data

    How to combine train_test_split() with prediction methods

    In addition, you’ll get information on related tools from sklearn.model_selection.

    Free Bonus: Click here to get access to a free NumPy Resources Guide that points you to the best tutorials, videos, and books for improving your NumPy skills.

    The Importance of Data Splitting

    Supervised machine learning is about creating models that precisely map the given inputs (independent variables, or predictors) to the given outputs (dependent variables, or responses).

    How you measure the precision of your model depends on the type of a problem you’re trying to solve. In regression analysis, you typically use the coefficient of determination, root-mean-square error, mean absolute error, or similar quantities. For classification problems, you often apply accuracy, precision, recall, F1 score, and related indicators.

    The acceptable numeric values that measure precision vary from field to field. You can find detailed explanations from Statistics By Jim, Quora, and many other resources.

    What’s most important to understand is that you usually need unbiased evaluation to properly use these measures, assess the predictive performance of your model, and validate the model.

    This means that you can’t evaluate the predictive performance of a model with the same data you used for training. You need evaluate the model with fresh data that hasn’t been seen by the model before. You can accomplish that by splitting your dataset before you use it.

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    Training, Validation, and Test Sets

    Splitting your dataset is essential for an unbiased evaluation of prediction performance. In most cases, it’s enough to split your dataset randomly into three subsets:

    The training set is applied to train, or fit, your model. For example, you use the training set to find the optimal weights, or coefficients, for linear regression, logistic regression, or neural networks.The validation set is used for unbiased model evaluation during hyperparameter tuning. For example, when you want to find the optimal number of neurons in a neural network or the best kernel for a support vector machine, you experiment with different values. For each considered setting of hyperparameters, you fit the model with the training set and assess its performance with the validation set.The test set is needed for an unbiased evaluation of the final model. You shouldn’t use it for fitting or validation.

    In less complex cases, when you don’t have to tune hyperparameters, it’s okay to work with only the training and test sets.

    Underfitting and Overfitting

    Splitting a dataset might also be important for detecting if your model suffers from one of two very common problems, called underfitting and overfitting:

    Underfitting is usually the consequence of a model being unable to encapsulate the relations among data. For example, this can happen when trying to represent nonlinear relations with a linear model. Underfitted models will likely have poor performance with both training and test sets.Overfitting usually takes place when a model has an excessively complex structure and learns both the existing relations among data and noise. Such models often have bad generalization capabilities. Although they work well with training data, they usually yield poor performance with unseen (test) data.

    You can find a more detailed explanation of underfitting and overfitting in Linear Regression in Python.

    स्रोत : realpython.com

    Train

    The train-test split procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not used to train the model. It is a fast and easy procedure to perform, the results of which allow you to compare the performance of machine learning algorithms for your predictive […]

    Train-Test Split for Evaluating Machine Learning Algorithms

    by Jason Brownlee on July 24, 2020 in Python Machine Learning

    Last Updated on August 26, 2020

    The train-test split procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not used to train the model.

    It is a fast and easy procedure to perform, the results of which allow you to compare the performance of machine learning algorithms for your predictive modeling problem. Although simple to use and interpret, there are times when the procedure should not be used, such as when you have a small dataset and situations where additional configuration is required, such as when it is used for classification and the dataset is not balanced.

    In this tutorial, you will discover how to evaluate machine learning models using the train-test split.

    After completing this tutorial, you will know:

    The train-test split procedure is appropriate when you have a very large dataset, a costly model to train, or require a good estimate of model performance quickly.

    How to use the scikit-learn machine learning library to perform the train-test split procedure.

    How to evaluate machine learning algorithms for classification and regression using the train-test split.

    Kick-start your project with my new book Machine Learning Mastery With Python, including step-by-step tutorials and the Python source code files for all examples.

    Let’s get started.

    Train-Test Split for Evaluating Machine Learning Algorithms

    Photo by Paul VanDerWerf, some rights reserved.

    Tutorial Overview

    This tutorial is divided into three parts; they are:

    Train-Test Split Evaluation

    When to Use the Train-Test Split

    How to Configure the Train-Test Split

    Train-Test Split Procedure in Scikit-Learn

    Repeatable Train-Test Splits

    Stratified Train-Test Splits

    Train-Test Split to Evaluate Machine Learning Models

    Train-Test Split for Classification

    Train-Test Split for Regression

    Train-Test Split Evaluation

    The train-test split is a technique for evaluating the performance of a machine learning algorithm.

    It can be used for classification or regression problems and can be used for any supervised learning algorithm.

    The procedure involves taking a dataset and dividing it into two subsets. The first subset is used to fit the model and is referred to as the training dataset. The second subset is not used to train the model; instead, the input element of the dataset is provided to the model, then predictions are made and compared to the expected values. This second dataset is referred to as the test dataset.

    Train Dataset: Used to fit the machine learning model.Test Dataset: Used to evaluate the fit machine learning model.

    The objective is to estimate the performance of the machine learning model on new data: data not used to train the model.

    This is how we expect to use the model in practice. Namely, to fit it on available data with known inputs and outputs, then make predictions on new examples in the future where we do not have the expected output or target values.

    The train-test procedure is appropriate when there is a sufficiently large dataset available.

    When to Use the Train-Test Split

    The idea of “sufficiently large” is specific to each predictive modeling problem. It means that there is enough data to split the dataset into train and test datasets and each of the train and test datasets are suitable representations of the problem domain. This requires that the original dataset is also a suitable representation of the problem domain.

    A suitable representation of the problem domain means that there are enough records to cover all common cases and most uncommon cases in the domain. This might mean combinations of input variables observed in practice. It might require thousands, hundreds of thousands, or millions of examples.

    Conversely, the train-test procedure is not appropriate when the dataset available is small. The reason is that when the dataset is split into train and test sets, there will not be enough data in the training dataset for the model to learn an effective mapping of inputs to outputs. There will also not be enough data in the test set to effectively evaluate the model performance. The estimated performance could be overly optimistic (good) or overly pessimistic (bad).

    If you have insufficient data, then a suitable alternate model evaluation procedure would be the k-fold cross-validation procedure.

    In addition to dataset size, another reason to use the train-test split evaluation procedure is computational efficiency.

    Some models are very costly to train, and in that case, repeated evaluation used in other procedures is intractable. An example might be deep neural network models. In this case, the train-test procedure is commonly used.

    Alternately, a project may have an efficient model and a vast dataset, although may require an estimate of model performance quickly. Again, the train-test split procedure is approached in this situation.

    Samples from the original training dataset are split into the two subsets using random selection. This is to ensure that the train and test datasets are representative of the original dataset.

    स्रोत : machinelearningmastery.com

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    Mohammed 6 day ago
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