我在尝试对从UCI机器学习数据库中找到的文本识别数据进行k最近邻预测。（https://archive.ics.uci.edu/ml/datasets/Letter+Recognition）
我已经对数据进行了交叉验证，并测试了准确性，没有问题，但无法运行classifier.predict()。谁能解释一下我为什么会得到这个错误？我查看了sklearn网站上关于维度灾难的资料，但我很难真正修复我的代码。
到目前为止，我的代码如下：
import pandas as pdimport numpy as npfrom sklearn import preprocessing, cross_validation, neighborsdf = pd.read_csv('KMeans_letter_recog.csv') X = np.array(df.drop(['Letter'], 1))y = np.array(df['Letter'])X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size = 0.2) #20% data usedclf = neighbors.KNeighborsClassifier()clf.fit(X_train, y_train)accuracy = clf.score(X_test, y_test) #testprint(accuracy) #this works fineexample = np.array([7,4,3,2,4,5,3,6,7,4,2,3,5,6,8,4])example = X.reshape(len(example), -1)prediction = clf.predict(example)print(prediction) #errordf.head() 输出如下：
Letter x-box y-box box_width box_height on_pix x-bar_mean \0 T 2 8 3 5 1 8 1 I 5 12 3 7 2 10 2 D 4 11 6 8 6 10 3 N 7 11 6 6 3 5 4 G 2 1 3 1 1 8 y-bar_mean x2bar_mean y2bar_mean xybar_mean x2y_mean xy2_mean \0 13 0 6 6 10 8 1 5 5 4 13 3 9 2 6 2 6 10 3 7 3 9 4 6 4 4 10 4 6 6 6 6 5 9 x-ege xegvy y-ege yegvx 0 0 8 0 8 1 2 8 4 10 2 3 7 3 9 3 6 10 2 8 4 1 7 5 10 我的错误信息如下：
Traceback (most recent call last): File "C:\Users\jai_j\Desktop\Python Projects\K Means ML.py", line 31, in <module> prediction = clf.predict(example) File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\neighbors\classification.py", line 145, in predict neigh_dist, neigh_ind = self.kneighbors(X) File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\neighbors\base.py", line 381, in kneighbors for s in gen_even_slices(X.shape[0], n_jobs) File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib\parallel.py", line 758, in __call__ while self.dispatch_one_batch(iterator): File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib\parallel.py", line 608, in dispatch_one_batch self._dispatch(tasks) File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib\parallel.py", line 571, in _dispatch job = self._backend.apply_async(batch, callback=cb) File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 109, in apply_async result = ImmediateResult(func) File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 326, in __init__ self.results = batch() File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in __call__ return [func(*args, **kwargs) for func, args, kwargs in self.items] File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in <listcomp> return [func(*args, **kwargs) for func, args, kwargs in self.items] File "sklearn\neighbors\binary_tree.pxi", line 1294, in sklearn.neighbors.kd_tree.BinaryTree.query (sklearn\neighbors\kd_tree.c:11325)ValueError: query data dimension must match training data dimension提前感谢任何帮助，我会继续寻找答案。
回答：
你的问题在于你没有正确地重塑example，并且你重塑的维度不正确。你将X数组重塑为(16, N)，其中N是X中的观测数量。
因此，当你尝试在example上进行预测时，你实际上是在使用你的分类器对重塑为N列的X进行预测，而不是像你训练时那样有16列。
看起来你想对单个示例进行预测，所以你应该重塑example而不是X。你可能想要example = example.reshape(1, -1)而不是example = X.reshape(len(example), -1)。
最初，你创建了形状为(16,)的example。你应该将它重塑为(1, 16)，通过使用(1, -1)作为维度。这样会得到一个形状为(1, 16)的数组，这符合你的分类器的要求。
明确地说，试着将你的代码改成这样：
example = np.array([7,4,3,2,4,5,3,6,7,4,2,3,5,6,8,4])example = example.reshape(1, -1)prediction = clf.predict(example)print(prediction) # 应该不会再报错