How to create and optimize a baseline ElasticNet Regression model in python

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

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

What Users are saying..

profile image

Ray han

Tech Leader | Stanford / Yale University
linkedin profile url

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

Relevant Projects

NLP Project on LDA Topic Modelling Python using RACE Dataset
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.

MLOps Project on GCP using Kubeflow for Model Deployment
MLOps using Kubeflow on GCP - Build and deploy a deep learning model on Google Cloud Platform using Kubeflow pipelines in Python

Build an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

Build a Multi ClassText Classification Model using Naive Bayes
Implement the Naive Bayes Algorithm to build a multi class text classification model in Python.

Build an AI Chatbot from Scratch using Keras Sequential Model
In this NLP Project, you will learn how to build an AI Chatbot from Scratch using Keras Sequential Model.

Build Deep Autoencoders Model for Anomaly Detection in Python
In this deep learning project , you will build and deploy a deep autoencoders model using Flask.

Build an optimal End-to-End MLOps Pipeline and Deploy on GCP
Learn how to build and deploy an end-to-end optimal MLOps Pipeline for Loan Eligibility Prediction Model in Python on GCP

Build a Customer Churn Prediction Model using Decision Trees
Develop a customer churn prediction model using decision tree machine learning algorithms and data science on streaming service data.

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

Build a Music Recommendation Algorithm using KKBox's Dataset
Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.