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

How to tune Hyper parameters using Random Search in Python?

How to tune Hyper parameters using Random Search in Python?

This recipe helps you tune Hyper parameters using Random Search in Python

0
This data science python source code does the following: 1. Different methods for Hyperparameter tuning a model. 2. Implements of RandomSearhCV using Cross Validation method. 3. Setting up parameters for RandomSearchCV. 4. Obtaining the best parameters and best result.
In [1]:
## How to tune Hyper-parameters using Random Search in Python
def Snippet_143():
    print()
    print(format('How to tune Hyper-parameters using Random Search in Python','*^82'))

    import warnings
    warnings.filterwarnings("ignore")

    # load libraries
    from scipy.stats import uniform
    from sklearn import linear_model, datasets
    from sklearn.model_selection import RandomizedSearchCV

    # Load data
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    # Create logistic regression
    logistic = linear_model.LogisticRegression()

    # Create Hyperparameter Search Space
    # Create regularization penalty space
    penalty = ['l1', 'l2']

    # Create regularization hyperparameter distribution using uniform distribution
    C = uniform(loc=0, scale=4)

    # Create hyperparameter options
    hyperparameters = dict(C=C, penalty=penalty)

    # Create randomized search 5-fold cross validation and 100 iterations
    clf = RandomizedSearchCV(logistic, hyperparameters, random_state=1, n_iter=100,
                             cv=5, verbose=0, n_jobs=-1)

    # Fit randomized search
    best_model = clf.fit(X, y)

    # View best hyperparameters
    print('Best Penalty:', best_model.best_estimator_.get_params()['penalty'])
    print('Best C:', best_model.best_estimator_.get_params()['C'])

Snippet_143()
************How to tune Hyper-parameters using Random Search in Python************
Best Penalty: l1
Best C: 1.668088018810296

Relevant Projects

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.

Identifying Product Bundles from Sales Data Using R Language
In this data science project in R, we are going to talk about subjective segmentation which is a clustering technique to find out product bundles in sales data.

Sequence Classification with LSTM RNN in Python with Keras
In this project, we are going to work on Sequence to Sequence Prediction using IMDB Movie Review Dataset​ using Keras in Python.

Data Science Project on Wine Quality Prediction in R
In this R data science project, we will explore wine dataset to assess red wine quality. The objective of this data science project is to explore which chemical properties will influence the quality of red wines.

Mercari Price Suggestion Challenge Data Science Project
Data Science Project in Python- Build a machine learning algorithm that automatically suggests the right product prices.

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.

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.

Human Activity Recognition Using Smartphones Data Set
In this deep learning project, you will build a classification system where to precisely identify human fitness activities.

Machine Learning project for Retail Price Optimization
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.

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.