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Models on scikit-lean don´t learn

HomeCategory: stackoverflowModels on scikit-lean don´t learn
Avatarkundan asked 3 months ago

I`m new with scikit-learn. I’m following the book “hands-on ml with scikit learn and tensorflow” All the models I try to implement perform quite poorly.

I don’t receive any error and the code is pretty much the same from the book.
I installed the same scikit version of the book, just in case, it was that.

I’m using the default hyperparameters and the models aren`t chosen really well for the task. But they perform far worse than in the book and just above random.

I think it might be because I`m using a laptop which is not powerful and the models stop training prematurely.

I’ve tried LinearRegression, RandomForestRegressor, SVR in a housing database of 20000 cases, with 12 parameters.
I`ve also tried an SGDClassifier on the MINST dataset as a binary classifier.
All of that following the book instructions.

#RANDOM FOREST MODEL
from sklearn.ensemble import RandomForestRegressor
forest_reg = RandomForestRegressor()
forest_reg.fit(housing_pr, housing_labels)
forest_reg_scores = cross_val_score(forest_reg, housing_pr, housing_labels, scoring="neg_mean_squared_error", cv=10)
forest_reg_rmse_scores = np.sqrt(-forest_reg_scores)
display_scores(forest_reg_rmse_scores)

The result is

Scores: [100358.84813795  59740.95594336  73069.35686091  58367.36656326
  70119.66693956  61570.40051825  49889.14813703  80314.78172767
  73177.26056318 102031.12922303]
Mean: 72863.89146141837
Standard deviation: 16454.877060423143

While on the book the mean is 52634,191, and the standard deviation 1576

The price range is between 120000 and 265000.

The difference is a lot larger on the MINST dataset. Doing a binary classifier to predict classify 5 it explains precission and recall. His are 0.7687 and 0.79136 while mine are 0.092217 and 0.06972.

1 Answers
Best Answer
AvatarArben answered 3 months ago
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