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Machine Learning for OpenCV
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Now let's turn our attention to a regression problem. As I'm sure you can recite in your sleep by now, regression is all about predicting continuous outcomes rather than predicting discrete class labels.
The easiest regression model is called linear regression. The idea behind linear regression is to describe a target variable (such as Boston house pricing) with a linear combination of features.
To keep things simple, let's just focus on two features. Let's say we want to predict tomorrow's stock prices using two features: today's stock price and yesterday's stock price. We will denote today's stock price as the first feature f1, and yesterday's stock price as f2. Then the goal of linear regression would be to learn two weight coefficients, w1 and w2, so that we can predict tomorrow's stock price as follows:
Here, ŷ is the prediction of tomorrow's ground truth
stock price y.
The special case of having only...