Key Concepts

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Scikit-Learn Logistic Regression Implementation

Logistic Regression sigmoid function

Classification Threshold definition

Logistic Regression interpretability

Log-Odds calculation

Logistic Regression Classifier

Logistic Regression prediction

Logistic Regression cost function

Scikit-Learn Logistic Regression Implementation

Scikit-Learn has a Logistic Regression implementation that fits a model to a set of training data and can classify new or test data points into their respective classes. All important parameters can be specified, as the norm used in penalizations and the solver used in optimization.

Logistic Regression
Lesson 1 of 1
  1. 1
    Introduction
    When an email lands in your inbox, how does your email service know whether it’s a real email or spam? This evaluation is made billions of times per day, and one way it can be done is with Logistic…
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  2. 2
    Linear Regression Approach
    With the data from Codecademy University, we want to predict whether each student will pass their final exam. And the first step to making that prediction is to predict the probability of each stud…
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  3. 3
    Logistic Regression
    We saw that the output of a Linear Regression model does not provide the probabilities we need to predict whether a student passes the final exam. Step in Logistic Regression! In Logistic Re…
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  4. 4
    Log-Odds
    In Linear Regression we multiply the coefficients of our features by their respective feature values and add the intercept, resulting in our prediction, which can range from -∞ to +∞. In Logistic R…
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  5. 5
    Sigmoid Function
    How did our Logistic Regression model create the S-shaped curve we previously saw? The answer is the Sigmoid Function. The Sigmoid Function is a special case of the more general _Logistic…
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  6. 6
    Log-Loss I
    Now that we understand how a Logistic Regression model makes its probability predictions, what coefficients and intercept should we use in our model to best predict whether a student will pass the …
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  7. 7
    Log Loss II
    J(\mathbf{b}) = -\frac{1}{m}\sum_{i=1}^{m} [y^{(i)}log(h(z^{(i)})) + (1-y^{(i)})log(1-h(z^{(i)}))] Let’s go ahead and break down our log-loss function into two separate parts so it begins to make…
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  8. 8
    Classification Thresholding
    Many machine learning algorithms, including Logistic Regression, spit out a classification probability as their result. Once we have this probability, we need to make a decision on what class the s…
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  9. 9
    Scikit-Learn
    Now that you know the inner workings of how Logistic Regression works, let’s learn how to easily and quickly create Logistic Regression models with sklearn! [sklearn](http://scikit-learn.org/stable…
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  10. 10
    Feature Importance
    One of the defining features of Logistic Regression is the interpretability we have from the feature coefficients. How to handle interpreting the coefficients depends on the kind of data you are wo…
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  11. 11
    Review
    Congratulations! You just learned how a Logistic Regression model works and how to fit one to a dataset. Class is over, and the final exam for Codecademy University’s Introductory Machine Learning …
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