Machine Learning with uncertainty quantification and interpretability.
If R is not installed automatically when running pip, install it manually.
Development version
!pip install git+https://github.com/Techtonique/learningmachine_python.git --verbose Stable version
!pip install learningmachine --verboseimport learningmachine as lm
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from time import time
from sklearn.metrics import mean_squared_error
# Regression (linear)
fit_obj = lm.BaseRegressor()
diabetes = load_diabetes()
X = diabetes.data[:150]
y = diabetes.target[:150]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=1213)
start = time()
fit_obj.fit(X_train, y_train)
print("Elapsed time: ", time() - start)
## Compute RMSE
rms1 = sqrt(mean_squared_error(y_test, fit_obj.predict(X_test), squared=False))
print(rms1)
