Blog Archives

Logistic Regression (1)

Nowadays, logistic regression is one of the most popular and most widely used classification algorithm. Here are some classification examples when logistic regression can help us: Email: Spam/Not Spam? Online Transactions: Fraudulent (Yes/No)? Tumor: Malignant/Benign? Advertisements

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Normal equation and non-invertibility

Normal equation: What if XTX is non-invertible? (singular/degenerate) R: ginv(X’*X)*X’y from {MASS} Octave: pinv(X’*X)*X’y

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Normal Equation

In this article I want to make a brief introduction to normal equation method. The article is following the Coursera structure, summarizing the following aspects: some theoretical points two practical examples general case advantages and disadvantages between gradient descent and normal equation

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Features and Polynomial Regression

Houses prices prediction Let’s say we want to forecast the price of the house. So, we have two features – the frontage of the house and the depth of the house.

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Gradient descent for linear regression (1)

A very common method used for defining a model and estimating its parameters, it is to minimize model’s errors with Gradient Descend. The Gradient Descent estimates the weights of the model in many iterations by minimizing a cost function at every step.

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