質問編集履歴
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・predicted_labelsは、初期設定しているもののその後にデータが与えれておらず空の状態なので、test_dataYはエラーメッセージからすると21,000件なのでそれに見合った件数の予測結果を引数に設定する方法を教えてください。
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エラー
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```python
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ValueError Traceback (most recent call last)
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・今の状態だとK=1の時でしか混合行列がわからないのでKが１以外の時を指定して表示させるにはどうすればいいですか
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30 if __name__ == '__main__':
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---> 31 main()
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<ipython-input-5-f6ad643389d7> in main()
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22 # 正解率を計算
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---> 23 score = accuracy_score(test_dataY, predicted_labels)
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24 print("正解率:{}".format(score))
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~\Anaconda3\lib\site-packages\sklearn\metrics\classification.py in accuracy_score(y_true, y_pred, normalize, sample_weight)
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175 # Compute accuracy for each possible representation
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--> 176 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
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177 if y_type.startswith('multilabel'):
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178 differing_labels = count_nonzero(y_true - y_pred, axis=1)
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~\Anaconda3\lib\site-packages\sklearn\metrics\classification.py in _check_targets(y_true, y_pred)
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69 y_pred : array or indicator matrix
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70 """
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---> 71 check_consistent_length(y_true, y_pred)
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72 type_true = type_of_target(y_true)
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73 type_pred = type_of_target(y_pred)
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~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
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202 if len(uniques) > 1:
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203 raise ValueError("Found input variables with inconsistent numbers of"
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--> 204 " samples: %r" % [int(l) for l in lengths])
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ValueError: Found input variables with inconsistent numbers of samples: [21000, 1]
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```
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def main():
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# 特徴データとラベルデータを取り出す
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targets = mnist.target
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#データを分割
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train_dataX, test_dataX, train_dataY, test_dataY = model_selection.train_test_split(features,targets,test_size=0.3)
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# 検証する近傍数
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K = 1
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ks = range(1, K + 1)
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for k in ks:
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predicted_labels = []
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# モデルを学習
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model = KNeighborsClassifier(n_neighbors=1, metric='euclidean')
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model.fit(train_dataX,train_dataY)
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model.fit(train_dataX,train_dataY)
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# 一つだけ取り除いたテストデータを識別
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# 一つだけ取り除いたテストデータを識別
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predicted_label = model.predict(test_dataX)
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predicted_label = model.predict(test_dataX)
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predicted_labels.append(predicted_label)
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predicted_labels.append(predicted_label)
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# 正解率を計算
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score = accuracy_score(test_dataY, predicted_label
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score = accuracy_score(test_dataY, predicted_label)
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print("正解率:{}".format(score))
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# 混合行列を表示
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cm = confusion_matrix(test_dataY, predicted_label
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cm = confusion_matrix(test_dataY, predicted_label)
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print(cm)
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