How to tune Hyper parameters using Grid Search in Python?

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 = y =

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 =, 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

Relevant Projects

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

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.

Predict Census Income using Deep Learning Models
In this project, we are going to work on Deep Learning using H2O to predict Census income.

Learn to prepare data for your next machine learning project
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

Deep Learning with Keras in R to Predict Customer Churn
In this deep learning project, we will predict customer churn using Artificial Neural Networks and learn how to model an ANN in R with the keras deep learning package.

Choosing the right Time Series Forecasting Methods
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.

Ecommerce product reviews - Pairwise ranking and sentiment analysis
This project analyzes a dataset containing ecommerce product reviews. The goal is to use machine learning models to perform sentiment analysis on product reviews and rank them based on relevance. Reviews play a key role in product recommendation systems.

Ensemble Machine Learning Project - All State Insurance Claims Severity Prediction
In this ensemble machine learning project, we will predict what kind of claims an insurance company will get. This is implemented in python using ensemble machine learning algorithms.

Demand prediction of driver availability using multistep time series analysis
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

Zillow’s Home Value Prediction (Zestimate)
Data Science Project in R -Build a machine learning algorithm to predict the future sale prices of homes.