can you please share some simple, but meaningful toy example like pycorels
to make sure it works
for example like
https://github.com/fingoldin/pycorels
Toy dataset (See picture example above)
from corels import CorelsClassifier
["loud", "samples"] is the most verbose setting possible
C = CorelsClassifier(max_card=2, c=0.0, verbosity=["loud", "samples"])
4 samples, 3 features
X = [[1, 0, 1], [0, 0, 0], [1, 1, 0], [0, 1, 0]]
y = [1, 0, 0, 1]
Feature names
features = ["Mac User", "Likes Pie", "Age < 20"]
Fit the model
C.fit(X, y, features=features, prediction_name="Has a dirty computer")
Print the resulting rulelist
print(C.rl())
Predict on the training set
print(C.predict(X))
can you please share some simple, but meaningful toy example like pycorels
to make sure it works
for example like
https://github.com/fingoldin/pycorels
Toy dataset (See picture example above)
from corels import CorelsClassifier
["loud", "samples"] is the most verbose setting possible
C = CorelsClassifier(max_card=2, c=0.0, verbosity=["loud", "samples"])
4 samples, 3 features
X = [[1, 0, 1], [0, 0, 0], [1, 1, 0], [0, 1, 0]]
y = [1, 0, 0, 1]
Feature names
features = ["Mac User", "Likes Pie", "Age < 20"]
Fit the model
C.fit(X, y, features=features, prediction_name="Has a dirty computer")
Print the resulting rulelist
print(C.rl())
Predict on the training set
print(C.predict(X))