How to tune Hyper parameters using Grid Search in Python?
HYPERPARAMETER TUNING DATA CLEANING PYTHON DATA MUNGING MACHINE LEARNING RECIPES PANDAS CHEATSHEET     ALL TAGS

How to tune Hyper parameters using Grid Search in Python?

How to tune Hyper parameters using Grid Search in Python?

This recipe helps you tune Hyper parameters using Grid Search 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.

To get the best set of hyperparameters we can use Grid Search. Grid Search passes all combinations of hyperparameters one by one into the model and check the result. Finally it gives us the set of hyperparemeters which gives the best result after passing in the model.

So this recipe is a short example of how can tune Hyper-parameters using Grid Search in Python

Step 1 - Import the library - GridSearchCv

import numpy as np from sklearn import linear_model, datasets from sklearn.model_selection import GridSearchCV

Here we have imported various modules like datasets, linear_model 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 wine dataset and we have created objects X and y to store the data and the target value respectively. dataset = datasets.load_wine() X = dataset.data y = dataset.target

Step 3 - Using Model

Here, we are using Logistic Regression as a Machine Learning model to use GridSearchCV. So we have created an object Logistic. logistic = linear_model.LogisticRegression()

Step 5 - Parameters to be optimized

Logistic Regression requires two parameters "C" and "penalty" to be optimised by GridSearchCV. So we have set these two parameters as a list of values form which GridSearchCV will select the best value of parameter. C = np.logspace(0, 4, 10) penalty = ["l1", "l2"] hyperparameters = dict(C=C, penalty=penalty)

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 for GridSearchCV and fitting the dataset i.e X and y clf = GridSearchCV(logistic, hyperparameters, cv=5, verbose=0) best_model = clf.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 Penalty:", best_model.best_estimator_.get_params()["penalty"]) print("Best C:", best_model.best_estimator_.get_params()["C"]) As an output we get:

Best Penalty: l1
Best C: 59.94842503189409

Download Materials

Relevant Projects

Churn Prediction in Telecom using Machine Learning in R
Estimating churners before they discontinue using a product or service is extremely important. In this ML project, you will develop a churn prediction model in telecom to predict customers who are most likely subject to churn.

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.

Locality Sensitive Hashing Python Code for Look-Alike Modelling
In this deep learning project, you will find similar images (lookalikes) using deep learning and locality sensitive hashing to find customers who are most likely to click on an ad.

Time Series Python Project using Greykite and Neural Prophet
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.

Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Customer Churn Prediction Analysis using Ensemble Techniques
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

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.

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.

Inventory Demand Forecasting using Machine Learning in R
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.