Hi, Sasha,
So based on your Y array, it seems that you have 6 training samples. Now, in
scikit-learn, each sample is represented as a row in the X array (and the
columns are the “features” — you have 1 feature here, right?).
Now, if you do
> X = X.reshape(-1, 1)
> X.shape
your array will have the shape (1, 5), but what you want is (5, 1). Thus, the
solution is to do
> X = X.reshape(-1, 1)
The complete code would be then
import numpy as np
X = np.array([10, 20, 30, 60, 108])
y = np.array([11, 23, 43, 170.5, 934.6])
X = X.reshape(-1, 1)
model.fit(X, y)
There’s also a problem in your predict call since you only have 6 samples, you
can’t have a “12” in there, only values from 0, 5. E.g.,
print("Predict for number 6 {}".format(model.predict([5])))
Predict for number 6 [-146.58922645]
Best,
Sebastian
> On Feb 6, 2016, at 11:52 AM, Sasha Kacanski <skacan...@gmail.com> wrote:
>
> X = np.array([10, 20, 30, 60, 108])
> Y = np.array([11, 23, 43, 170.5, 934.6])
> model = LinearRegression()
> x = X.reshape(-1,1)
> model.fit(x,y)
> print("Predict for number 12 {}".format(model.predict([12])[0]))
>
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