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K najbliższych sąsiadów – kto z kim przestaje, takim się staje
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"source": "# Kod 1\n \nimport pandas as pd\nfrom sklearn.datasets import load_breast_cancer\n \nfrom sklearn.neighbors import KNeighborsClassifier\n \nfrom sklearn.model_selection import train_test_split\n \nbreast_cancer = load_breast_cancer()\nX = pd.DataFrame(breast_cancer[\"data\"], \n columns = breast_cancer[\"feature_names\"])\ny = pd.Series(breast_cancer[\"target\"])\n \nX_train, X_test, y_train, y_test = train_test_split(X, y, \n test_size = 0.25, \n random_state = 42)",
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"source": "# Kod 2\n\nknn = KNeighborsClassifier()\nknn.fit(X = X_train, y = y_train)\n\ntrain_score = knn.score(X = X_train, y = y_train)\ntest_score = knn.score(X = X_test, y = y_test)\n\ntrain_score, test_score",
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"source": "%%time\n# Kod 3\n\n\nfrom sklearn.model_selection import GridSearchCV\n\nparam_grid = {'n_neighbors': range(1, 101),\n 'weights': [\"uniform\", \"distance\"],\n 'p': [1, 2]\n }\n\nknn_grid_search = GridSearchCV(estimator = KNeighborsClassifier(), param_grid = param_grid, cv = 5, iid = False)\n\nknn_grid_search.fit(X = X_train, y = y_train)\n\ngrid_train_score = knn_grid_search.score(X = X_train, y = y_train)\ngrid_test_score = knn_grid_search.score(X = X_test, y = y_test)\n\nprint(grid_train_score, grid_test_score)\nprint(knn_grid_search.best_params_)",
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"text": "0.9436619718309859 0.972027972027972\n{'n_neighbors': 6, 'p': 1, 'weights': 'uniform'}\nCPU times: user 1min 8s, sys: 164 ms, total: 1min 9s\nWall time: 1min 9s\n"
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