Optimization of hyper parameters for logistic regression in Python

In this recipe how to optimize hyper parameters of a Logistic Regression model using Grid Search and implementation of various functions is given using 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.

This data science python source code does the following:
1. Hyper-parameters of logistic regression.
2. Implements Standard Scaler function on the dataset.
3. Performs train_test_split on your dataset.
4. Uses Cross Validation to prevent overfitting.

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 to use Grid Search and get the best set of hyperparameters.

Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects

 

Step 1 - Import the library - GridSearchCv

import numpy as np from sklearn import linear_model, decomposition, datasets from sklearn.pipeline import Pipeline from sklearn.model_selection import GridSearchCV 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 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 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 Logistic Regression as a Machine Learning model to use GridSearchCV. So we have created an object Logistic_Reg. logistic_Reg = linear_model.LogisticRegression()

Step 4 - Using Pipeline for GridSearchCV

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 logistic_Reg. pipe = Pipeline(steps=[('std_slc', std_slc), ('pca', pca), ('logistic_Reg', logistic_Reg)])

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))

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(-4, 4, 50) penalty = ['l1', 'l2']

Now we are creating a dictionary to set all the parameters options for different modules. parameters = dict(pca__n_components=n_components, logistic_Reg__C=C, logistic_Reg__penalty=penalty)

 

Explore More Data Science and Machine Learning Projects for Practice. Fast-Track Your Career Transition with ProjectPro

Step 5 - 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_GS for GridSearchCV and fitting the dataset i.e X and y clf_GS = GridSearchCV(pipe, parameters) clf_GS.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:', clf_GS.best_estimator_.get_params()['logistic_Reg__penalty']) print('Best C:', clf_GS.best_estimator_.get_params()['logistic_Reg__C']) print('Best Number Of Components:', clf_GS.best_estimator_.get_params()['pca__n_components']) print(); print(clf_GS.best_estimator_.get_params()['logistic_Reg']) As an output we get:

Best Penalty: l1
Best C: 109.85411419875572
Best Number Of Components: 13

LogisticRegression(C=109.85411419875572, class_weight=None, dual=False,
          fit_intercept=True, intercept_scaling=1, max_iter=100,
          multi_class='warn', n_jobs=None, penalty='l1', random_state=None,
          solver='warn', tol=0.0001, verbose=0, warm_start=False)

Join Millions of Satisfied Developers and Enterprises to Maximize Your Productivity and ROI with ProjectPro - Read ProjectPro Reviews Now!

Download Materials

What Users are saying..

profile image

Anand Kumpatla

Sr Data Scientist @ Doubleslash Software Solutions Pvt Ltd
linkedin profile url

ProjectPro is a unique platform and helps many people in the industry to solve real-life problems with a step-by-step walkthrough of projects. A platform with some fantastic resources to gain... Read More

Relevant Projects

Time Series Project to Build a Multiple Linear Regression Model
Learn to build a Multiple linear regression model in Python on Time Series Data

End-to-End Speech Emotion Recognition Project using ANN
Speech Emotion Recognition using RAVDESS Audio Dataset - Build an Artificial Neural Network Model to Classify Audio Data into various Emotions like Sad, Happy, Angry, and Neutral

Build an Image Segmentation Model using Amazon SageMaker
In this Machine Learning Project, you will learn to implement the UNet Architecture and build an Image Segmentation Model using Amazon SageMaker

Deep Learning Project- Real-Time Fruit Detection using YOLOv4
In this deep learning project, you will learn to build an accurate, fast, and reliable real-time fruit detection system using the YOLOv4 object detection model for robotic harvesting platforms.

PyCaret Project to Build and Deploy an ML App using Streamlit
In this PyCaret Project, you will build a customer segmentation model with PyCaret and deploy the machine learning application using Streamlit.

Recommender System Machine Learning Project for Beginners-1
Recommender System Machine Learning Project for Beginners - Learn how to design, implement and train a rule-based recommender system in Python

Many-to-One LSTM for Sentiment Analysis and Text Generation
In this LSTM Project , you will build develop a sentiment detection model using many-to-one LSTMs for accurate prediction of sentiment labels in airline text reviews. Additionally, we will also train many-to-one LSTMs on 'Alice's Adventures in Wonderland' to generate contextually relevant text.

Microsoft Fabric Project to Build a Financial Reporting Agent
In this Microsoft Fabric project, you'll build a financial reporting agent that simplifies data management, automates analysis, and delivers real-time dashboards for wealth advisors and their clients.

Build OCR from Scratch Python using YOLO and Tesseract
In this deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.

Loan Eligibility Prediction Project using Machine learning on GCP
Loan Eligibility Prediction Project - Use SQL and Python to build a predictive model on GCP to determine whether an application requesting loan is eligible or not.