LinearDiscriminantAnalysis on Dataset
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# Load libraries
from sklearn import datasets
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
# Load the Iris flower dataset:
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Create an LDA that will reduce the data down to 1 feature
lda = LinearDiscriminantAnalysis(n_components=2)
# run an LDA and use it to transform the features
X_lda = lda.fit(X, y).transform(X)
lda
# Print the number of features
print(‘Original number of features:’, X.shape[1])
print(‘Reduced number of features:’, X_lda.shape[1])
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Standard Scalar on lda with Iris Data
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from sklearn.preprocessing import StandardScaler
X = StandardScaler().fit_transform(iris.data)
# Create an LDA that will reduce the data down to 1 feature
lda = LinearDiscriminantAnalysis(n_components=2)
Y_lda = lda.fit_transform(X,y)
# Show results
print(‘Original number of features:’, X.shape[1])
print(‘Reduced number of features:’, Y_lda.shape[1])
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Variance Thresholding For Feature Selection
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from sklearn import datasets
from sklearn.feature_selection import VarianceThreshold
# Load the Iris flower dataset:
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Create VarianceThreshold object with a variance with a threshold of 0.5
thresholder = VarianceThreshold(threshold=0.3)
# Conduct variance thresholding
X_high_variance = thresholder.fit_transform(iris.data)
iris.data
X_high_variance
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Train and Test dataset creation
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X = list(range(15))
print (X)
y = [x * x for x in X]
print (y)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, train_size=0.65,test_size=0.35, random_state=10)
print (“X_train: “, X_train)
print (“y_train: “, y_train)
print(“X_test: “, X_test)
print (“y_test: “, y_test)
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Test and Train dataset creation from iris dataset
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# Load libraries
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
# Load the digits dataset
iris = datasets.load_iris()
# Create the features matrix
X = iris.data
# Create the target vector
y = iris.target
# Create training and test sets
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.1,
random_state=1)
X_train
X_test
y_train
y_test
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Creating Standardizer with test and train dataset
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# Create standardizer
standardizer = StandardScaler()
# Fit standardizer to training set
standardizer.fit(X_train)
# Apply to both training and test sets
X_train_std = standardizer.transform(X_train)
X_test_std = standardizer.transform(X_test)
X_train_std
X_test_std
