Valueerror: expected 2d array, got 1d array instead:- how can i circumvent the error?

Question

I get an error:

Expected 2D array, got 1D array instead:

when I try to run the following code:

import numpy as np
 import matplotlib.pyplot as plt
 from matplotlib import style
 
 style.use("ggplot")
 from sklearn import svm
 
 x = [1, 5, 1.5, 8, 1, 9]
 y = [2, 8, 1.8, 8, 0.6, 11]
 
 plt.scatter(x,y)
 plt.show()
 
 X = np.array([[1,2],
  [5,8],
  [1.5,1.8],
  [8,8],
  [1,0.6],
  [9,11]])
 
 y = [0,1,0,1,0,1]
 X.reshape(1, -1)
 
 clf = svm.SVC(kernel='linear', C = 1.0)
 clf.fit(X,y)
 
 print(clf.predict([0.58,0.76]))

X.reshape(1, -1)

How to fix the valueerror: expected 2d array, got 1d array instead:. Please give me some good ideas.

lyytutoria · Accepted Answer

The cause: This error occurs because the predict method is only supposed to be provided with the same 2D array but with the one value you wish to process. Solution: You can simply replace.

[0.58,0.76]

With

[[0.58,0.76]]

Ethan Charbonnier · Answer

This problem occurs when you run prediction against the array [0.58,0.76]. You can fix the problem by changing its shape before calling predict()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style

style.use("ggplot")
from sklearn import svm

x = [1, 5, 1.5, 8, 1, 9]
y = [2, 8, 1.8, 8, 0.6, 11]

plt.scatter(x,y)
plt.show()

X = np.array([[1,2],
 [5,8],
 [1.5,1.8],
 [8,8],
 [1,0.6],
 [9,11]])

y = [0,1,0,1,0,1]

clf = svm.SVC(kernel='linear', C = 1.0)
clf.fit(X,y)

test = np.array([0.58, 0.76])
print test # Produces: [ 0.58 0.76]
print test.shape # Produces: (2,) meaning 2 rows, 1 col

test = test.reshape(1, -1)
print test # Produces: [[ 0.58 0.76]]
print test.shape # Produces (1, 2) meaning 1 row, 2 cols

print(clf.predict(test)) # Produces [0], as expected

Sign Up

Sign In

Forgot Password

ITtutoria Latest Questions

Valueerror: expected 2d array, got 1d array instead:- how can i circumvent the error?