How to create and optimize a baseline ElasticNet Regression model?
REGRESSION EXAMPLES DATA CLEANING PYTHON DATA MUNGING MACHINE LEARNING RECIPES PANDAS CHEATSHEET     ALL TAGS

How to create and optimize a baseline ElasticNet Regression model?

How to create and optimize a baseline ElasticNet Regression model?

This recipe helps you create and optimize a baseline ElasticNet Regression model

Recipe Objective

Many a times while working on a dataset and using a Machine Learning model we don't know which set of hyperparameters will give us the best result. Passing all sets of hyperparameters manually through the model and checking the result might be a hectic work and may not be possible to do.

Here we will be creating elasticnet regressor model and will use gridsearchCV to optimize the parameters.

This data science python source code does the following:
1. Imports necessary libraries needed for elastic net.
2. Tuning the parameters of Elasstic net regression.
3. Performns train_test_split and crossvalidation on your dataset.

So this recipe is a short example of how we can create and optimize a baseline ElasticNet Regression model

Step 1 - Import the library - GridSearchCv

from sklearn import decomposition, datasets from sklearn import linear_model from sklearn.pipeline import Pipeline from sklearn.model_selection import GridSearchCV, cross_val_score from sklearn.preprocessing import StandardScaler

Here we have imported various modules like decomposition, datasets,linear_model, Pipeline, StandardScaler and GridSearchCV from differnt libraries. We will understand the use of these later while using it in the in the code snipet.
For now just have a look on these imports.

Step 2 - Setup the Data

Here we have used datasets to load the inbuilt iris dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_iris() X = dataset.data y = dataset.target

Step 3 - Using StandardScaler and PCA

StandardScaler is used to remove the outliners and scale the data by making the mean of the data 0 and standard deviation as 1. So we are creating an object std_scl to use standardScaler. std_slc = StandardScaler()

We are also using Principal Component Analysis(PCA) which will reduce the dimension of features by creating new features which have most of the varience of the original data. pca = decomposition.PCA()

Here, we are using ElasticNet as a Machine Learning model and will use GridSearchCV. So we have created an object elasticnet. elasticnet = linear_model.ElasticNet()

Step 5 - Using Pipeline and defining Parameters

Pipeline will helps us by passing modules one by one through GridSearchCV for which we want to get the best parameters. So we are making an object pipe to create a pipeline for all the three objects std_scl, pca and elasticnet. pipe = Pipeline(steps=[('stc_slc', stc_slc), ('pca', pca), ('elasticnet', elasticnet)])

Now we have to define the parameters that we want to optimise for these three objects.
StandardScaler doesnot requires any parameters to be optimised by GridSearchCV.
Principal Component Analysis requires a parameter 'n_components' to be optimised. 'n_components' signifies the number of components to keep after reducing the dimension. n_components = list(range(1,X.shape[1]+1,1))

Elasticnet requires two parameters 'normalize' and 'selection' to be optimised by GridSearchCV. So we have set these two parameters to pass from GridSearchCV will select the best value of parameter. normalize = [True, False] selection = ['cyclic', 'random']

Now we are creating a dictionary to set all the parameters options for different objects. parameters = dict(pca__n_components=n_components, elasticnet__normalize=normalize, elasticnet__selection=selection)

Step 6 - Using GridSearchCV and Printing Results

Before using GridSearchCV, lets have a look on the important parameters.

  • estimator: In this we have to pass the models or functions on which we want to use GridSearchCV
  • param_grid: Dictionary or list of parameters of models or function in which GridSearchCV have to select the best.
  • Scoring: It is used as a evaluating metric for the model performance to decide the best hyperparameters, if not especified then it uses estimator score.
Making an object clf_EN for GridSearchCV and fitting the dataset i.e X and y clf_EN = GridSearchCV(pipe, parameters) clf_EN.fit(X, y) Now we are using print statements to print the results. It will give the values of hyperparameters as a result. print('Best Number Of Components:', clf_EN.best_estimator_.get_params()['pca__n_components']) print(clf_EN.best_estimator_.get_params()['elasticnet']) As an output we get:

Best Number Of Components: 1

ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,
      max_iter=1000, normalize=True, positive=False, precompute=False,
      random_state=None, selection='cyclic', tol=0.0001, warm_start=False)

Download Materials

Relevant Projects

Human Activity Recognition Using Multiclass Classification in Python
In this human activity recognition project, we use multiclass classification machine learning techniques to analyse fitness dataset from a smartphone tracker.

Avocado Machine Learning Project Python for Price Prediction
In this ML Project, you will use the Avocado dataset to build a machine learning model to predict the average price of avocado which is continuous in nature based on region and varieties of avocado.

Predict Employee Computer Access Needs in Python
Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

Topic modelling using Kmeans clustering to group customer reviews
In this Kmeans clustering machine learning project, you will perform topic modelling in order to group customer reviews based on recurring patterns.

Data Science Project-TalkingData AdTracking Fraud Detection
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

German Credit Dataset Analysis to Classify Loan Applications
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.

Medical Image Segmentation Deep Learning Project
In this deep learning project, you will learn to implement Unet++ models for medical image segmentation to detect and classify colorectal polyps.

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

Build OCR from Scratch Python using YOLO and Tesseract
In this deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.

Build a Face Recognition System in Python using FaceNet
In this deep learning project, you will build your own face recognition system in Python using OpenCV and FaceNet by extracting features from an image of a person's face.