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Polars.pdf
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Supervised Learning: Classification and Regression
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
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Best Data Science Archive Notes
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๐๐จ๐ ๐ข๐ฌ๐ญ๐ข๐ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง ๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง๐๐ ๐ฌ๐ข๐ฆ๐ฉ๐ฅ๐ฒ
If youโve just started learning Machine Learning, ๐๐จ๐ ๐ข๐ฌ๐ญ๐ข๐ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง is one of the most important and misunderstood algorithms.
Hereโs everything you need to know ๐
๐ โจ ๐๐ก๐๐ญ ๐ข๐ฌ ๐๐จ๐ ๐ข๐ฌ๐ญ๐ข๐ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง?
Itโs 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).
๐ โจ ๐๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌ?
It starts like Linear Regression, but instead of outputting continuous values, it passes the result through a ๐ฌ๐ข๐ ๐ฆ๐จ๐ข๐ ๐๐ฎ๐ง๐๐ญ๐ข๐จ๐ง to map the result between 0 and 1.
๐๐ณ๐ฐ๐ฃ๐ข๐ฃ๐ช๐ญ๐ช๐ต๐บ = ๐ / (๐ + ๐โป(๐ฐ๐ฑ + ๐))
Here,
๐ฐ = weights
๐ฑ = inputs
๐ = bias
๐ = Eulerโs number (approx. 2.718)
๐ โจ ๐๐ก๐ฒ ๐ง๐จ๐ญ ๐๐ข๐ง๐๐๐ซ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง?
Because Linear Regression predicts any number from -โ to +โ, which doesnโt make sense for probability.
We need outputs between 0 and 1 and thatโs where the sigmoid function helps.
๐ โจ ๐๐จ๐ฌ๐ฌ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง ๐ฎ๐ฌ๐๐?
๐๐ข๐ง๐๐ซ๐ฒ ๐๐ซ๐จ๐ฌ๐ฌ-๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ
โ = โ(y log(p) + (1 โ y) log(1 โ p))
Where y is the actual value (0 or 1), and p is the predicted probability
๐ โจ ๐๐ฉ๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐ซ๐๐๐ฅ ๐ฅ๐ข๐๐:
๐๐ฆ๐๐ข๐ฅ ๐๐ฉ๐๐ฆ ๐๐๐ญ๐๐๐ญ๐ข๐จ๐ง
๐๐ข๐ฌ๐๐๐ฌ๐ ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐๐ฎ๐ฌ๐ญ๐จ๐ฆ๐๐ซ ๐๐ก๐ฎ๐ซ๐ง ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐๐ฅ๐ข๐๐ค-๐๐ก๐ซ๐จ๐ฎ๐ ๐ก ๐๐๐ญ๐ ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐๐ข๐ง๐๐ซ๐ฒ ๐ฌ๐๐ง๐ญ๐ข๐ฆ๐๐ง๐ญ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง
๐ โจ ๐๐ฌ. ๐๐ญ๐ก๐๐ซ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐ซ๐ฌ
Itโs fast, interpretable, and easy to implement, but it struggles with non-linearly separable data unlike Decision Trees or SVMs.
๐ โจ ๐๐๐ง ๐ข๐ญ ๐ก๐๐ง๐๐ฅ๐ ๐ฆ๐ฎ๐ฅ๐ญ๐ข๐ฉ๐ฅ๐ ๐๐ฅ๐๐ฌ๐ฌ๐๐ฌ?
Yes, using One-vs-Rest (OvR) or Softmax in Multinomial Logistic Regression.
๐ โจ ๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐ ๐ข๐ง ๐๐ฒ๐ญ๐ก๐จ๐ง
If youโve just started learning Machine Learning, ๐๐จ๐ ๐ข๐ฌ๐ญ๐ข๐ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง is one of the most important and misunderstood algorithms.
Hereโs everything you need to know ๐
๐ โจ ๐๐ก๐๐ญ ๐ข๐ฌ ๐๐จ๐ ๐ข๐ฌ๐ญ๐ข๐ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง?
Itโs 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).
๐ โจ ๐๐จ๐ฐ ๐ข๐ญ ๐ฐ๐จ๐ซ๐ค๐ฌ?
It starts like Linear Regression, but instead of outputting continuous values, it passes the result through a ๐ฌ๐ข๐ ๐ฆ๐จ๐ข๐ ๐๐ฎ๐ง๐๐ญ๐ข๐จ๐ง to map the result between 0 and 1.
๐๐ณ๐ฐ๐ฃ๐ข๐ฃ๐ช๐ญ๐ช๐ต๐บ = ๐ / (๐ + ๐โป(๐ฐ๐ฑ + ๐))
Here,
๐ฐ = weights
๐ฑ = inputs
๐ = bias
๐ = Eulerโs number (approx. 2.718)
๐ โจ ๐๐ก๐ฒ ๐ง๐จ๐ญ ๐๐ข๐ง๐๐๐ซ ๐๐๐ ๐ซ๐๐ฌ๐ฌ๐ข๐จ๐ง?
Because Linear Regression predicts any number from -โ to +โ, which doesnโt make sense for probability.
We need outputs between 0 and 1 and thatโs where the sigmoid function helps.
๐ โจ ๐๐จ๐ฌ๐ฌ ๐ ๐ฎ๐ง๐๐ญ๐ข๐จ๐ง ๐ฎ๐ฌ๐๐?
๐๐ข๐ง๐๐ซ๐ฒ ๐๐ซ๐จ๐ฌ๐ฌ-๐๐ง๐ญ๐ซ๐จ๐ฉ๐ฒ
โ = โ(y log(p) + (1 โ y) log(1 โ p))
Where y is the actual value (0 or 1), and p is the predicted probability
๐ โจ ๐๐ฉ๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐ซ๐๐๐ฅ ๐ฅ๐ข๐๐:
๐๐ฆ๐๐ข๐ฅ ๐๐ฉ๐๐ฆ ๐๐๐ญ๐๐๐ญ๐ข๐จ๐ง
๐๐ข๐ฌ๐๐๐ฌ๐ ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐๐ฎ๐ฌ๐ญ๐จ๐ฆ๐๐ซ ๐๐ก๐ฎ๐ซ๐ง ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐๐ฅ๐ข๐๐ค-๐๐ก๐ซ๐จ๐ฎ๐ ๐ก ๐๐๐ญ๐ ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐๐ข๐ง๐๐ซ๐ฒ ๐ฌ๐๐ง๐ญ๐ข๐ฆ๐๐ง๐ญ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง
๐ โจ ๐๐ฌ. ๐๐ญ๐ก๐๐ซ ๐๐ฅ๐๐ฌ๐ฌ๐ข๐๐ข๐๐ซ๐ฌ
Itโs fast, interpretable, and easy to implement, but it struggles with non-linearly separable data unlike Decision Trees or SVMs.
๐ โจ ๐๐๐ง ๐ข๐ญ ๐ก๐๐ง๐๐ฅ๐ ๐ฆ๐ฎ๐ฅ๐ญ๐ข๐ฉ๐ฅ๐ ๐๐ฅ๐๐ฌ๐ฌ๐๐ฌ?
Yes, using One-vs-Rest (OvR) or Softmax in Multinomial Logistic Regression.
๐ โจ ๐๐ฑ๐๐ฆ๐ฉ๐ฅ๐ ๐ข๐ง ๐๐ฒ๐ญ๐ก๐จ๐ง
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
pred = model.predict(X_test)
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๐๐ฐ Linear Algebra in Python: Matrix Inverses and Least Squares โ https://realpython.com/python-linear-algebra/
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