Nested Cross-Validation — Hyperparameter Optimization and Model Selection

Cross-Validation also referred to **as out of sampling technique **is an essential element of a data science project. It is a resampling procedure used to evaluate machine learning models and access how the model will perform for an independent test dataset.

Hyperparameter optimization or tuning is a process of choosing a set of hyperparameters for a machine learning algorithm that performs best for a particular dataset.

Both Cross-Validation and Hyperparameter Optimization is an important aspect of a data science project. Cross-validation is used to evaluate the performance of a machine learning algorithm and Hyperparameter tuning is used to find the best set of hyperparameters for that machine learning algorithm.

Model selection without nested cross-validation uses the same data to tune model parameters and evaluate model performance that may lead to an optimistically biased evaluation of the model. We get a poor estimation of errors in training or test data due to information leakage. To overcome this problem, Nested Cross-Validation comes into the picture.

Comparing the performance of non-nested and nested CV strategies for the Iris dataset using a Support Vector Classifier. You can observe the performance plot below, from this article.

#education #crossvalidation #machine-learning #data-science #artificial-intelligence

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Nested Cross-Validation — Hyperparameter Optimization and Model Selection
Hertha  Mayer

Hertha Mayer

1594769515

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.');
    }
}

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Rusty  Shanahan

Rusty Shanahan

1596960180

Comparison and Optimization on ML Models

In this article, we will elaborate on ML model selection, validation, and optimization using online loan application data. What you will learn is how to create, evaluate, and optimize ML models. Specifically, we will focus on Logistic Regression, Support Vector Machine, and Random Forest. It is split into 7 parts.

  1. Business challenge
  2. Data review
  3. EDA
  4. Data processing
  5. Model building
  6. Model validation
  7. Parameter tuning
  8. Takeaways

Now, let’s begin the journey.

1. Business challenge

We are tasked by a loan lending company to predict quality applicants. The job is to develop a model to predict the interest of applicants, by analyzing applicants’ data entered during the application process. If the applicant is interested, he or she will e-sign the product, otherwise not.

2. Data review

Quickly looking at the data shown in the video, you can find there are 21 columns with 17,908 rows. With so many features, let’s create a summary of each variable’s explanation, as shown in Figure 1, to enhance our understanding.

Video A brief view of the raw data

Image for post

Fig.1 Summary of variable definition

3. EDA

EDA is a must-to-do step before any data processing. Generally, it includes data cleaning, data visualization, and correlation analysis. Here I will not elaborate on EDA. If you want to familiarize yourself with the EDA steps, feel free to read this article.

4. Data processing

  1. Data engineering

During EDA data visualization, you may find for variable ‘months_employed’, most applicants have 0-month employment. This does not seem right, so we will drop this un-reliable column.

Second, two columns, ‘personal_account_m’ and ‘_personal_account_y’, _can be combined into a new variable to represent the total months of the applicant’s account. Specifically,

#machine-learning #data-science #model-optimization #binary-classification #model-selection #deep learning

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