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quadratic_discriminant_analysis.py

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+#LOAD NECESSARY LIBRARIES
+from sklearn.model_selection import train_test_split
+from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
+from sklearn.model_selection import RepeatedStratifiedKFold
+from sklearn.model_selection import cross_val_score
+from sklearn import datasets
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+
+#LOAD AND VIEW IRIS DATASET
+iris = datasets.load_iris()
+df = pd.DataFrame(data = np.c_[iris['data'], iris['target']],
+                 columns = iris['feature_names'] + ['target'])
+df['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)
+df.columns = ['s_length', 's_width', 'p_length', 'p_width', 'target', 'species']
+print(df.head())
+len(df.index)
+
+#DEFINE PREDICTOR AND RESPONSE VARIABLES
+X = df[['s_length', 's_width', 'p_length', 'p_width']]
+y = df['species']
+
+#FIT LDA MODEL
+model = QuadraticDiscriminantAnalysis()
+model.fit(X, y)
+
+#DEFINE METHOD TO EVALUATE MODEL
+cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
+
+#EVALUATE MODEL
+scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1)
+print(np.mean(scores))
+
+#USE MODEL TO MAKE PREDICTION ON NEW OBSERVATION
+new = [5, 3, 1, .4]
+model.predict([new])