### Linear regression with scikit learn

It’s been a while since the last post was written. So it’s time to create new one. I know, I promised to explain how to choose $ \alpha $ parameter and why it matters, but not this time.

Let’s solve our linear regression problem using some libraries which already have all algorithms implemented. It’s not necessary to implement all stuff manually, because it’s easy to make a mistake and sped tons of hours debugging code. With libraries you can just feed the data and get result. Moreover some kind of stuff is not as easy as linear regression (for instance Support Vector Machines) and to write gradient descent code for them, lot of time should be spent to solve math equations.

We will use Scikit Learn module. It already has Linear Regression model so we can just pass the data and get our $ \theta $ values returned.

I don’t want to copy/paste module documentation in here, you can open website and read everything. Only counterintuitive parts (that were for me, obviously) will be explained. Data preparation, vectors…etc are all the same. There is only thing that is differnt. Remember, for our model we added $ X_0 = 1 $ values as first column to get our hypothesis function looks like $ h_\theta(x) = \theta_0 + \theta_1 * x $ . In scikit learn there is no need to add that column to our data. We just pass $ X $ and $ Y $ column vectors to get model parameters.

All code for linear regression looks as:

```
from __future__ import print_function
import numpy as np
from sklearn import datasets, linear_model
data = np.loadtxt('ex1data1.txt', delimiter=',')
X = data[:, 0:1]
Y = data[:, 1:2]
regr = linear_model.LinearRegression()
regr.fit(X, Y)
print('Coefficients: Theta_0: {0}, Theta_1: {1}'.format(regr.intercept_[0], regr.coef_[0][0]))
```

Pretty easy, isn’t it? $ X, Y $ are column vectors, our $ \theta_0 $ value returned in the `intercept_`

property and
all other $ \theta $ values (we have only one for now, since we’re dealing with only one variable) are in the `coef_`

property.

And our model is: $$ h_\theta(x) = -3.89578087831 + 1.19303364419*x $$ which is pretty close to one we got by hand.