所以，我有一个数据集
zip我要去的地方
crop_land grazing_land forest_land fishing_ground built_up_land carbon total record
0 5.850442e-03 3.993524e-04 4.022381e-04 4.725790e-05 1.416867e-02 0.000000e+00 1.216222e-03 3
1 4.714311e-07 0.000000e+00 3.967481e-06 1.886327e-05 1.742382e-04 0.000000e+00 8.599662e-06 1
2 1.750920e-03 1.051690e-03 4.959930e-04 6.042567e-04 2.664088e-03 3.642014e-03 1.967436e-03 7
3 4.070420e-03 1.157143e-04 6.423243e-04 2.899980e-04 2.535550e-03 9.690789e-04 1.174554e-03 5
4 2.049992e-10 6.649159e-11 1.791842e-11 1.843842e-11 1.410455e-10 0.000000e+00 4.340002e-11 0
... ... ... ... ... ... ... ... ...
172 5.633361e-03 1.691959e-03 7.646393e-04 8.220462e-04 5.313993e-03 0.000000e+00 1.732825e-03 3
173 4.732318e-03 3.468817e-03 4.679903e-04 1.776410e-02 1.079035e-02 0.000000e+00 6.275626e-03 1
174 1.668830e-10 1.117230e-09 8.854867e-10 2.619004e-10 2.600211e-10 0.000000e+00 6.872388e-10 0
175 2.045367e-10 9.413857e-12 6.864525e-11 2.326519e-10 9.270079e-10 7.130137e-11 1.307066e-10 4
176 9.565469e-04 1.634472e-03 2.068556e-04 9.076553e-05 1.122083e-03 4.772406e-04 8.268765e-04 5
我明白了：
ValueError跟踪（最近一次通话最近） sklearn.metrics中的1导入retret_score，precision_score，precision_score，f1_score，confusion_matrix
from sklearn.metrics import recall_score, accuracy_score, precision_score, f1_score, confusion_matrix
new_predictions = log_reg.predict(normalised_test_df.iloc[:,2 ].values.reshape(177, -1 , 1))
cnf_mat = confusion_matrix(y_true=y_test, y_pred = new_predictions, labels=['2A','3A'])
ValueError：找到的数组具有暗3。预期的估计量<=2。我如何 无需切分并切回火车和测试即可处理此问题 值