How to tune Hyper parameters using Random Search in Python?

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

Access Face Recognition Project Code using Facenet in Python

Step 1 - Import the library - RandomizedSearchCv

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

Here we have imported various modules like datasets, uniform, linear_model and RandomizedSearchCV 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. iris = datasets.load_iris() X = iris.data y = iris.target

Step 3 - Using Model

Here, we are using Logistic Regression as a Machine Learning model to use RandomisedSearchCV. 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 RandomisedSearchCV. So we have set these two parameters as a list of values form which RandomisedSearchCV will select the best value of parameter. C = uniform(loc=0, scale=4) penalty = ["l1", "l2"] hyperparameters = dict(C=C, penalty=penalty)

Step 6 - Using RandomisedSearchCV and Printing Results

Before using RandomisedSearchCV, 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 RandomisedSearchCV
  • param_grid: Dictionary or list of parameters of models or function in which RandomisedSearchCV 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 RandomisedSearchCV and fitting the dataset i.e X and y clf = RandomizedSearchCV(logistic, hyperparameters, random_state=1, n_iter=100, cv=5, verbose=0, n_jobs=-1) 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: 1.668088018810296

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

Learn How to Build a Logistic Regression Model in PyTorch
In this Machine Learning Project, you will learn how to build a simple logistic regression model in PyTorch for customer churn prediction.

AWS MLOps Project for ARCH and GARCH Time Series Models
Build and deploy ARCH and GARCH time series forecasting models in Python on AWS .

Expedia Hotel Recommendations Data Science Project
In this data science project, you will contextualize customer data and predict the likelihood a customer will stay at 100 different hotel groups.

Recommender System Machine Learning Project for Beginners-2
Recommender System Machine Learning Project for Beginners Part 2- Learn how to build a recommender system for market basket analysis using association rule mining.

GCP MLOps Project to Deploy ARIMA Model using uWSGI Flask
Build an end-to-end MLOps Pipeline to deploy a Time Series ARIMA Model on GCP using uWSGI and Flask

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.

Build and Deploy Text-2-SQL LLM Using OpenAI and AWS
In this LLM project, you will learn to build a user-friendly web application that leverages Large Language Models (LLMs) to convert natural language queries into optimized SQL commands.

PyTorch Project to Build a LSTM Text Classification Model
In this PyTorch Project you will learn how to build an LSTM Text Classification model for Classifying the Reviews of an App .

NLP Project for Multi Class Text Classification using BERT Model
In this NLP Project, you will learn how to build a multi-class text classification model using using the pre-trained BERT model.

MLOps Project to Deploy Resume Parser Model on Paperspace
In this MLOps project, you will learn how to deploy a Resume Parser Streamlit Application on Paperspace Private Cloud.