Linear Discriminant Analysis (LDA) in Python – Step 7.) 3×3 Confusion Matrix for Regression Model with LDA

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# Import the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Import the dataset
dataset = pd.read_csv(‘LDA_Data.csv’)
X = dataset.iloc[:, 0:13].values
y = dataset.iloc[:, 13].values

# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)

# Feature Scaling to Dataset
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Implement LDA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
lda = LDA(n_components = 2)
X_train = lda.fit_transform(X_train, y_train)
X_test = lda.transform(X_test)

# Train Logistic Regression with LDA
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train)

# Predict Results of Regression with LDA
y_pred = classifier.predict(X_test)

# Confusion Matrix 3X3
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

 

Other Topics – Multivariate Analysis : 
Other Topics – Association Rule : 
Other Topics – Artifical Inteligent : 
  • Upper Confidence Bound
  • Thompson Sampling

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