𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 𝐬𝐢𝐦𝐩𝐥𝐲

If you’ve just started learning Machine Learning, 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 is one of the most important and misunderstood algorithms.

Here’s everything you need to know 👇

𝟏 ⇨ 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐋𝐨𝐠𝐢𝐬𝐭𝐢𝐜 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?

It’s a supervised ML algorithm used to predict probabilities and classify data into binary outcomes (like 0 or 1, Yes or No, Spam or Not Spam).

𝟐 ⇨ 𝐇𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬?

It starts like Linear Regression, but instead of outputting continuous values, it passes the result through a 𝐬𝐢𝐠𝐦𝐨𝐢𝐝 𝐟𝐮𝐧𝐜𝐭𝐢𝐨𝐧 to map the result between 0 and 1.

𝘗𝘳𝘰𝘣𝘢𝘣𝘪𝘭𝘪𝘵𝘺 = 𝟏 / (𝟏 + 𝐞⁻(𝐰𝐱 + 𝐛))

Here,
𝐰 = weights
𝐱 = inputs
𝐛 = bias
𝐞 = Euler’s number (approx. 2.718)

𝟑 ⇨ 𝐖𝐡𝐲 𝐧𝐨𝐭 𝐋𝐢𝐧𝐞𝐚𝐫 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧?

Because Linear Regression predicts any number from -∞ to +∞, which doesn’t make sense for probability.
We need outputs between 0 and 1 and that’s where the sigmoid function helps.

𝟒 ⇨ 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐮𝐬𝐞𝐝?

𝐁𝐢𝐧𝐚𝐫𝐲 𝐂𝐫𝐨𝐬𝐬-𝐄𝐧𝐭𝐫𝐨𝐩𝐲

ℒ = −(y log(p) + (1 − y) log(1 − p))
Where y is the actual value (0 or 1), and p is the predicted probability

𝟓 ⇨ 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐥𝐢𝐟𝐞:

𝐄𝐦𝐚𝐢𝐥 𝐒𝐩𝐚𝐦 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧
𝐃𝐢𝐬𝐞𝐚𝐬𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐂𝐡𝐮𝐫𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐂𝐥𝐢𝐜𝐤-𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐑𝐚𝐭𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧
𝐁𝐢𝐧𝐚𝐫𝐲 𝐬𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧

𝟔 ⇨ 𝐕𝐬. 𝐎𝐭𝐡𝐞𝐫 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫𝐬

It’s fast, interpretable, and easy to implement, but it struggles with non-linearly separable data unlike Decision Trees or SVMs.

𝟕 ⇨ 𝐂𝐚𝐧 𝐢𝐭 𝐡𝐚𝐧𝐝𝐥𝐞 𝐦𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐜𝐥𝐚𝐬𝐬𝐞𝐬?

Yes, using One-vs-Rest (OvR) or Softmax in Multinomial Logistic Regression.

𝟖 ⇨ 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧

from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
pred = model.predict(X_test)

#LogisticRegression #MachineLearning #MLAlgorithms #SupervisedLearning #BinaryClassification #SigmoidFunction #PythonML #ScikitLearn #MLForBeginners #DataScienceBasics #MLExplained #ClassificationModels #AIApplications #PredictiveModeling #MLRoadmap

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