Logistic Regression As mentioned earlier, classification problems can be categorized into binary problems and multi-class problems. Binary problems are the types of problems that logistic regression is typically used to solve. In binary classification, the target variable yiyi belongs to one of two classes: 0 or 1. These classes are often referred to as “negative”Continue reading “ML Zoomcamp 2023 – Machine Learning for Classification– Part 9”
Tag Archives: Linear regression
ML Zoomcamp 2023 – Machine Learning for Regression – Part 6
Training a linear regression model From the last article we know that we need to multiply the feature matrix X with weights vector w to get y (the prediction for price). g(X) = Xw ~ y Actually we want this Xw to be equal to y, but often it’s not possible.To achieve this, we needContinue reading “ML Zoomcamp 2023 – Machine Learning for Regression – Part 6”
ML Zoomcamp 2023 – Machine Learning for Regression – Part 5
Linear regression vector form This article covers the generalization to a vector form of what we did in the last article. That means coming back from only one observation xi (of one car) to the whole feature matrix X. Looking at the last part of this formula we see the dot product (vector-vector multiplication). g(xi)Continue reading “ML Zoomcamp 2023 – Machine Learning for Regression – Part 5”
ML Zoomcamp 2023 – Machine Learning for Regression – Part 4
Linear regression Let’s delve deeper into the topic of linear regression. Linear regression is a fundamental statistical technique used in the field of machine learning for solving regression problems. In simple terms, regression analysis involves predicting a continuous outcome variable based on one or more input features. That means the output of the model isContinue reading “ML Zoomcamp 2023 – Machine Learning for Regression – Part 4”
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