Sebastian,
Tanks so much, perfectly explained and proof that I missed completely on
understanding how to do this...
Lot's of learning ahead ...
Thanks much again,
On Sat, Feb 6, 2016 at 2:28 PM, Sebastian Raschka <se.rasc...@gmail.com>
wrote:
> 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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Aleksandar Kacanski
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