I'm following this tutorial to make this ML prediction:
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]))
I'm using Python 3.6 and I get error "Expected 2D array, got 1D array instead:" I think the script is for older versions, but I don't know how to convert it to the 3.6 version.
Already try with the:
X.reshape(1, -1)
You are just supposed to provide the predict
method with the same 2D array, but with one value that you want to process (or more). In short, you can just replace
[0.58,0.76]
With
[[0.58,0.76]]
And it should work.
EDIT: This answer became popular so I thought I'd add a little more explanation about ML. The short version: we can only use predict
on data that is of the same dimensionality as the training data (X
) was.
In the example in question, we give the computer a bunch of rows in X
(with 2 values each) and we show it the correct responses in y
. When we want to predict
using new values, our program expects the same - a bunch of rows. Even if we want to do it to just one row (with two values), that row has to be part of another array.