Question

我正在尝试使用简单命令：iris数据集上的train_test_split并使用svm进行预测，但是当我使用“fit”时如下：

dat_iris = datasets.load_iris()
x1 = dat_iris.data[:,2]
y1 = dat_iris.target
x_train,y_train,x_test,y_test = train_test_split(x1, y1, test_size = 0.3, 
random_state=0)
svm_model = SVC(kernel='linear',C=1.0, random_state=0)
svm_model.fit(x_train,y_train)
y_pred = svm_model.predict(x_train)

但出现以下错误：

ValueError                                Traceback (most recent call last)
<ipython-input-245-120527f222b3> in <module>()
      7 
      8 svm_model = SVC(kernel='linear',C=1.0, random_state=0)
----> 9 svm_model.fit(x_train,y_train)
     10 y_pred = svm_model.predict(x_train)
     11 metrics.classification_report(y_pred, y_train)

~/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py in fit(self, X, y, sample_weight)
    147         self._sparse = sparse and not callable(self.kernel)
    148 
--> 149         X, y = check_X_y(X, y, dtype=np.float64, order='C', accept_sparse='csr')
    150         y = self._validate_targets(y)
    151 

~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in check_X_y(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
    550         y = y.astype(np.float64)
    551 
--> 552     check_consistent_length(X, y)
    553 
    554     return X, y

~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in check_consistent_length(*arrays)
    171     if len(uniques) > 1:
    172         raise ValueError("Found input variables with inconsistent numbers of"
--> 173                          " samples: %r" % [int(l) for l in lengths])
    174 
    175 

ValueError: Found input variables with inconsistent numbers of samples: [105, 45]

由于目标或输入的大小，可能会出现这种情况，如何解决此问题？

Answer 1

您混合了返回参数的顺序。它应该是：

X_train, X_test, y_train, y_test = train_test_split(x1, y1, test_size = 0.3, 
random_state=0)

通过sklearn.train_test_split（）从iris数据制作训练集合时出错

1 个答案: