How does Linear Discriminant Analysis work?
MACHINE LEARNING RECIPES DATA CLEANING PYTHON DATA MUNGING PANDAS CHEATSHEET     ALL TAGS

How does Linear Discriminant Analysis work?

How does Linear Discriminant Analysis work?

This recipe explains how Linear Discriminant Analysis work

0

Recipe Objective

Linear Discriminant Analysis is a classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes' rule. It fits a Gaussian density to each class, assuming that all classes share the same covariance matrix.

So this recipe is a short example on how does Linear Discriminant Analysis work. Let's get started.

Step 1 - Import the library

from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.datasets import load_iris from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

Let's pause and look at these imports. We have exported train_test_split which helps in randomly breaking the datset in two parts. Here sklearn.dataset is used to import one classification based model dataset. Also, we have exported LinearDiscriminantAnalysis to build our model.

Step 2 - Setup the Data

X,y=load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

Here, we have used load_iris function to import our dataset in two list form (X and y) and therefore kept return_X_y to be True. Further with have broken down the dataset into 2 parts, train and test with ratio 3:4.

Now our dataset is ready.

Step 3 - Building the model

model = LinearDiscriminantAnalysis()

We have simply built a classification model with LinearDiscriminantAnalysis with default values.

Step 4 - Fit the model and predict for test set

model.fit(X_train, y_train) y_pred= model.predict(X_test)

Here we have simply fit used fit function to fit our model on X_train and y_train. Now, we are predicting the values of X_test using our built model.

Step 5 - Printing the accuracy

print(model.score(X_train,y_train)) print(model.score(X_test,y_test))

Here we have calculated accuracy score using score function for both our train and test set.

Step 6 - Lets look at our dataset now

Once we run the above code snippet, we will see:

0.9821428571428571
0.9210526315789473

Clearly, the model built for the given datset is efficient on any unknown set.

Relevant Projects

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 an Image Classifier for Plant Species Identification
In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

Data Science Project - Instacart Market Basket Analysis
Data Science Project - Build a recommendation engine which will predict the products to be purchased by an Instacart consumer again.

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.

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.

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.

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.

Resume parsing with Machine learning - NLP with Python OCR and Spacy
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.

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.

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