Value Error: Expected 2D array, got 1D array instead:-CodePudding

While practicing Support Vector Regression Model I got this error

Here is my data set:

Here is independent variable X:

Here is dependent variable Y:

Here is X_train

Here Is Y_train

Error body:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-40-2de23298b092> in <module>()
----> 1 sc_y.inverse_transform(regressor.predict(sc_X.transform([[6.5]])))

1 frames
/usr/local/lib/python3.7/dist-packages/sklearn/preprocessing/_data.py in inverse_transform(self, X, copy)
   1020             estimator=self,
   1021             dtype=FLOAT_DTYPES,
-> 1022             force_all_finite="allow-nan",
   1023         )
   1024 

/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    763                     "Reshape your data either using array.reshape(-1, 1) if "
    764                     "your data has a single feature or array.reshape(1, -1) "
--> 765                     "if it contains a single sample.".format(array)
    766                 )
    767 

ValueError: Expected 2D array, got 1D array instead:
array=[0.01150915].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

My code:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

dataset = pd.read_csv('Position_Salaries.csv')
X = dataset.iloc[:, 1:-1].values
y = dataset.iloc[:, -1].values
y = y.reshape(len(y), 1)

from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y)

from sklearn.svm import SVR
regressor = SVR(kernel = 'rbf')
regressor.fit(X, y)

sc_y.inverse_transform(regressor.predict(sc_X.transform([[6.5]])))

CodePudding user response：

The issue is with the test input [[6.5]] which you are providing to sc_X.transform()) in the last line of your code.

Try to find the shape of X and test input using numpy.shape and make sure that number of dimensions should be 2 in both cases. So you will get something like (Xi, Xj), (Ti, Tj). Also, Xj must be same as Tj (basically number of features must match for the train and test data)

CodePudding user response：

If your dataset has 8 columns, here is how you pick X and y

values = dataset.values
X = values[:,0:8]
y = values[:,8]

take it from there, note too that values is deprecated, instead it is to_numpy()