Implement Logistic Regression Class for binary classification

[dabasajay]

Implement the LogisticRegression Class for binary classification problems.

LogisticRegression class should have these methods:

• .fit(X,y) :- it takes in features(X) and targets(y) and trains the model.
• .predict(X) :- it takes features(X) as input and outputs the predicted targets/labels(y)
• .predict_proba(X) :- it takes features(X) as input and outputs the predicted probabilities of targets/labels(y)

Note:

• .predict() can internally call .predict_proba() and then change probabilities to labels based on threshold(default to 0.5)

Test your model on IRIS dataset using 2 classes(instead of 3 classes of IRIS) available in the library and check how well it performs. Example code

from learnemall.datasets import iris
from learnemall.linear import LogisticRegression
model = LogisticRegression()
X,y = iris.load_dataset()
# Take 2 classes only
X = X [ : , :-2]
y = (y!=0)*1
# train the model
model.fit(X,y)
# predict using model
y_pred = model.predict(X)
# Evaluate
print((y_pred == y).mean())

[JARVVVIS]

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[Ayush789]

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