How to carry out k-fold cross-validation on an imbalanced classification problem

In this article, we would — state the appropriate criteria for applying the k-fold cross-validation on an imbalanced class distribution problem; and demonstrate how to implement that in python through Jupyter notebook.

This article will cover the following sections:

A Quick Overview Of The K-Fold Cross Validation

Overview Of Data Source

Rules For Correctly Applying The K-Fold Cross Validation On An Imbalanced Class Distribution Model

How To Apply A K-Fold Cross Validation On An Imbalanced Classification Problem In Python

A Quick Overview Of The K-Fold Cross-Validation

It is statistically unreliable to evaluate the performance of a model just once. It is best to repeat the performance evaluation multiple times to create a distribution of performance evaluation values, and then take a summary statistics (say mean and standard deviation)of the distribution. This ensures we get a true representative estimate of the true performance value of the model, together with the variance. This concept is called repeated random sampling.

The **k-fold cross-validation **(k-fold cv)makes use of the repeated random sampling technique to evaluate model performance by dividing the data into 5 or 10 equal folds and thereafter evaluating the performance of the model on each fold. Specifically, it evaluates the performance of a model by carrying out the following ordered steps:

1. Shuffle the main data,
2. Divide the main data into k groups without replacement,
3. Take the first group as a test data, and the remaining k-1 group as a training data set, then perform model performance evaluation,
4. Repeat step 3 above, for every other group (second group, third group… kth group) E.g Take the second group as a test data, and the remaining k-1 group as training data set, then perform model performance evaluation,
5. Score model performance( on accuracy, roc_auc, etc)for each k-fold cross-validation evaluation, and
6. Take the mean and variance of the distribution of scores to estimate the overall performance of the model.

The figure below highlights the number of cross-validation evaluations (5 evaluations carried out) carried out on a 5-fold split to evaluate a model performance:

In general and as observed from the figure above, each group of a k group split would be a test group once, and a member of a training data set k-1 times during model performance cross-validation evaluation.

#machine-learning #model #how-to #data-science #evaluation #deep learning

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Hands-On Implementation of K-Fold Cross-Validation and LOOCV in Machine Learning

In machine learning, while building a predictive model for some classification or regression task we always split the data set into two different parts that is training and testing. The training part is used to train the machine learning model whereas the testing part is used for predictions by the model. These predictions are then evaluated using different evaluation methods. But do you think if you are getting an 85% test accuracy you will get the same performance of the model on production data? Does it guarantee the same results? The answer to this question is No we cannot expect the same accuracy. We can just get close to it but not the same. Therefore we need a method that can tell us that this is the range of accuracy that we can expect when we will use the model in production.

This is where K-Fold cross-validation comes into the picture that helps us to give us an estimate of the model performance on unseen data. Often this method is used to give stakeholders an estimate of accuracy or the performance of the model when it will put in production.

Through this article, we will see what exactly is K-fold cross-validation, how it works, and then we will implement it on a data set to check the estimation of accuracy which we can expect on unseen data. For this experiment, we are using the Pima Indian Diabetes data set that can be downloaded from the Kaggle website.

#developers corner #cross-validation #k fold cv #machine learning

How to carry out k-fold cross-validation on an imbalanced classification problem

In this article, we would — state the appropriate criteria for applying the k-fold cross-validation on an imbalanced class distribution problem; and demonstrate how to implement that in python through Jupyter notebook.

This article will cover the following sections:

A Quick Overview Of The K-Fold Cross Validation

Overview Of Data Source

Rules For Correctly Applying The K-Fold Cross Validation On An Imbalanced Class Distribution Model

How To Apply A K-Fold Cross Validation On An Imbalanced Classification Problem In Python

A Quick Overview Of The K-Fold Cross-Validation

It is statistically unreliable to evaluate the performance of a model just once. It is best to repeat the performance evaluation multiple times to create a distribution of performance evaluation values, and then take a summary statistics (say mean and standard deviation)of the distribution. This ensures we get a true representative estimate of the true performance value of the model, together with the variance. This concept is called repeated random sampling.

The **k-fold cross-validation **(k-fold cv)makes use of the repeated random sampling technique to evaluate model performance by dividing the data into 5 or 10 equal folds and thereafter evaluating the performance of the model on each fold. Specifically, it evaluates the performance of a model by carrying out the following ordered steps:

1. Shuffle the main data,
2. Divide the main data into k groups without replacement,
3. Take the first group as a test data, and the remaining k-1 group as a training data set, then perform model performance evaluation,
4. Repeat step 3 above, for every other group (second group, third group… kth group) E.g Take the second group as a test data, and the remaining k-1 group as training data set, then perform model performance evaluation,
5. Score model performance( on accuracy, roc_auc, etc)for each k-fold cross-validation evaluation, and
6. Take the mean and variance of the distribution of scores to estimate the overall performance of the model.

The figure below highlights the number of cross-validation evaluations (5 evaluations carried out) carried out on a 5-fold split to evaluate a model performance:

In general and as observed from the figure above, each group of a k group split would be a test group once, and a member of a training data set k-1 times during model performance cross-validation evaluation.

#machine-learning #model #how-to #data-science #evaluation #deep learning

How to validate mobile phone number in laravel with example

Data validation and sanitization is a very important thing from security point of view for a web application. We can not rely on user’s input. In this article i will let you know how to validate mobile phone number in laravel with some examples.

if we take some user’s information in our application, so usually we take phone number too. And if validation on the mobile number field is not done, a user can put anything in the mobile number field and without genuine phone number, this data would be useless.

Since we know that mobile number can not be an alpha numeric or any alphabates aand also it should be 10 digit number. So here in this examples we will add 10 digit number validation in laravel application.

We will aalso see the uses of regex in the validation of mobile number. So let’s do it with two different way in two examples.

Example 1:

In this first example we will write phone number validation in HomeController where we will processs user’s data.

<?php

namespace App\Http\Controllers;

use Illuminate\Http\Request;
use App\User;

class HomeController extends Controller
{
    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function create()
    {
        return view('createUser');
    }

    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function store(Request $request)
    {
        $request->validate([
                'name' => 'required',
                'phone' => 'required|digits:10',
                'email' => 'required|email|unique:users'
            ]);

        $input = $request->all();
        $user = User::create($input);

        return back()->with('success', 'User created successfully.');
    }
}

Example 2:

In this second example, we will use regex for user’s mobile phone number validation before storing user data in our database. Here, we will write the validation in Homecontroller like below.

<?php

namespace App\Http\Controllers;

use Illuminate\Http\Request;
use App\User;
use Validator;

class HomeController extends Controller
{
    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function create()
    {
        return view('createUser');
    }

    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function store(Request $request)
    {
        $request->validate([
                'name' => 'required',
                'phone' => 'required|regex:/^([0-9\s\-\+\(\)]*)$/|min:10',
                'email' => 'required|email|unique:users'
            ]);

        $input = $request->all();
        $user = User::create($input);

        return back()->with('success', 'User created successfully.');
    }
}

#laravel #laravel phone number validation #laravel phone validation #laravel validation example #mobile phone validation in laravel #phone validation with regex #validate mobile in laravel

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