Delete 3217-classification-lr-example1.py

3217-classification-lr-example1.py

-#Taken from Scikit
-
-import matplotlib.pyplot as plt
-from sklearn.linear_model import LogisticRegression
-from sklearn import datasets
-from sklearn.inspection import DecisionBoundaryDisplay
-
-# import some data from a predefined datatset
-iris = datasets.load_iris()
-X = iris.data[:, :2]  # we only take the first two features.
-Y = iris.target
-#print shape of the array for X and Y. Also get value of targets
-print (X.shape)
-print (Y)
-print (Y.shape)
-
-
-# Create an instance of Logistic Regression Classifier and fit the data.
-logreg = LogisticRegression(C=1)
-logreg.fit(X, Y)
-
-_, ax = plt.subplots(figsize=(4, 3))
-DecisionBoundaryDisplay.from_estimator(
-    logreg,
-    X,
-    cmap=plt.cm.Paired,
-    ax=ax,
-    response_method="auto",
-    plot_method="pcolormesh",
-    shading="auto",
-    xlabel="Sepal length",
-    ylabel="Sepal width",
-    eps=0.5,
-)
-
-# Plot the training points
-plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors="k", cmap=plt.cm.Paired)
-
-
-plt.xticks(())
-plt.yticks(())
-
-plt.show()