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Project: Data 60
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import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.neighbors import KNeighborsClassifier from sklearn import datasets from sklearn.model_selection import train_test_split iris = datasets.load_iris() print(iris.keys()) # dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names']) X = iris.data y = iris.target # Setup arrays to store train and test accuracies neighbors = np.arange(1, 20) train_accuracy = np.empty(len(neighbors)) test_accuracy = np.empty(len(neighbors)) # Loop over different values of k for i, k in enumerate(neighbors): # Setup a k-NN Classifier with k neighbors: knn knn = KNeighborsClassifier(n_neighbors=k) # Fit the classifier to the training data knn.fit(X_train, y_train) #Compute accuracy on the training set train_accuracy[i] = knn.score(X_train, y_train) #Compute accuracy on the testing set test_accuracy[i] = knn.score(X_test, y_test) # Generate plot plt.title('k-NN: Varying Number of Neighbors') plt.plot(neighbors, test_accuracy, label = 'Testing Accuracy') plt.plot(neighbors, train_accuracy, label = 'Training Accuracy') plt.legend() plt.xlabel('Number of Neighbors') plt.ylabel('Accuracy') plt.show()
['target_names', 'data', 'target', 'DESCR', 'feature_names'] KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=1, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=2, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=3, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=4, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=5, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=6, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=7, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=8, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=9, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=10, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=11, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=12, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=13, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=14, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=15, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=16, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=17, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=18, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=19, p=2, weights='uniform') <matplotlib.text.Text object at 0x7fa17d401490> [<matplotlib.lines.Line2D object at 0x7fa17c61f890>] [<matplotlib.lines.Line2D object at 0x7fa17c61ff50>] <matplotlib.legend.Legend object at 0x7fa17c0f1ad0> <matplotlib.text.Text object at 0x7fa17cee4b90> <matplotlib.text.Text object at 0x7fa17c1c8b50>
import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.neighbors import KNeighborsClassifier from sklearn import datasets from sklearn.model_selection import train_test_split iris = datasets.load_iris() print(iris.keys()) # dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names']) X = iris.data y = iris.target # Setup arrays to store train and test accuracies neighbors = np.arange(1, 2) train_accuracy = np.empty(len(neighbors)) test_accuracy = np.empty(len(neighbors)) # Loop over different values of k for i, k in enumerate(neighbors): # Setup a k-NN Classifier with k neighbors: knn knn = KNeighborsClassifier(n_neighbors=k) # Fit the classifier to the training data knn.fit(X_train, y_train) #Compute accuracy on the training set train_accuracy[i] = knn.score(X_train, y_train) #Compute accuracy on the testing set test_accuracy[i] = knn.score(X_test, y_test) # Generate plot plt.title('k-NN: Varying Number of Neighbors') plt.plot(neighbors, test_accuracy, label = 'Testing Accuracy') plt.plot(neighbors, train_accuracy, label = 'Training Accuracy') plt.legend() plt.xlabel('Number of Neighbors') plt.ylabel('Accuracy') plt.show()
['target_names', 'data', 'target', 'DESCR', 'feature_names'] KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=1, p=2, weights='uniform') <matplotlib.text.Text object at 0x7fa17d0e1450> [<matplotlib.lines.Line2D object at 0x7fa17d143e50>] [<matplotlib.lines.Line2D object at 0x7fa17d143750>] <matplotlib.legend.Legend object at 0x7fa17d143810> <matplotlib.text.Text object at 0x7fa17d756390> <matplotlib.text.Text object at 0x7fa17c0b4990>
import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.neighbors import KNeighborsClassifier from sklearn import datasets from sklearn.model_selection import train_test_split iris = datasets.load_iris() print(iris.keys()) # dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names']) X = iris.data y = iris.target # Setup arrays to store train and test accuracies neighbors = np.arange(1, 50) train_accuracy = np.empty(len(neighbors)) test_accuracy = np.empty(len(neighbors)) # Loop over different values of k for i, k in enumerate(neighbors): # Setup a k-NN Classifier with k neighbors: knn knn = KNeighborsClassifier(n_neighbors=k) # Fit the classifier to the training data knn.fit(X_train, y_train) #Compute accuracy on the training set train_accuracy[i] = knn.score(X_train, y_train) #Compute accuracy on the testing set test_accuracy[i] = knn.score(X_test, y_test) # Generate plot plt.title('k-NN: Varying Number of Neighbors') plt.plot(neighbors, test_accuracy, label = 'Testing Accuracy') plt.plot(neighbors, train_accuracy, label = 'Training Accuracy') plt.legend() plt.xlabel('Number of Neighbors') plt.ylabel('Accuracy') plt.show()
['target_names', 'data', 'target', 'DESCR', 'feature_names'] KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=1, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=2, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=3, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=4, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=5, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=6, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=7, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=8, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=9, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=10, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=11, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=12, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=13, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=14, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=15, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=16, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=17, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=18, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=19, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=20, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=21, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=22, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=23, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=24, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=25, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=26, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=27, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=28, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=29, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=30, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=31, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=32, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=33, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=34, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=35, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=36, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=37, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=38, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=39, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=40, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=41, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=42, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=43, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=44, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=45, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=46, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=47, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=48, p=2, weights='uniform') KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=1, n_neighbors=49, p=2, weights='uniform') <matplotlib.text.Text object at 0x7fa17d5bf2d0> [<matplotlib.lines.Line2D object at 0x7fa17d69b9d0>] [<matplotlib.lines.Line2D object at 0x7fa17d69b8d0>] <matplotlib.legend.Legend object at 0x7fa17cad9d50> <matplotlib.text.Text object at 0x7fa17d1c5d90> <matplotlib.text.Text object at 0x7fa17d981990>