如何在scikit-learn中使用SGDRegressor

Question

I am trying to figure out how to properly use scikit-learn's SGDRegressor model. in order to fit to a dataset I need to call a function fit(X,y) where x is a numpy array of shape (n_samples,n_features), and y is a 1d numpy array of length n_samples. I am trying to figure out what y is supposed to represent.

for instance my data appears as so:

my features are years starting in 1972, and the values are a corresponding value for that year. I am trying to predict the values for years in the future such as 2008, or 2012. I am assuming that each row in my data should represent a row/sample in X where each element in that is the value for a year. in that case what would y be? I was thinking that y should just be the years, but then y would be of length n_features instead of n_samples. if y is to be of length n_samples then what could y possibly be that is of length 5(number of samples in the data shown below). I am thinking I must transform this data some way.

Answer 1

In machine learning, y represents the label or target of your data . That is, the correct answers for your training data ( X ).

If you want to learn some values corresponding to years, then those years will be your training data ( X ) and the correct values associated to them will be your targets ( y ).

You can notice that this fits the sizes you mentioned in your first paragraph: X will be of shape (n_samples, n_features) because it will have as many entries as you have years, and each entry will be of size 1 (you only have 1 feature, the year) and y will be of length n_samples because you have a value associated with each year.

Answer 2

y is your target (what you want to predict) and you can get it this way:

from sklearn import linear_model

clf = linear_model.SGDRegressor()
clf.fit(x_to_train, y_to_train)

# clf is a trained model

y_predicted = clf.predict(X_to_predict)