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The Hitchhiker’s Guide to Machine Learning in Python
The model takes in an instance and then goes down the tree, testing significant features against a determined conditional statement. Depending on the result, it will go down to the left or right child branch and onward after that. Typically the most significant features in the process will fall closer to the root of the tree. Decision trees are becoming increasingly popular and can serve as a strong learning algorithm for any data scientist to have in their repertoire , especially when coupled with techniques like random forests, boosting, and bagging.
Once again, use the video below for a more in-depth look into the underlying functionality of decision trees. Support vector machines, also known as SVM, are a well-known supervised classification algorithm that create a dividing line between the your differing categories of data.
The way this vector is calculated, in simple terms, is by optimizing the line so that the closest point in each of the groups will be farthest away from each other. K-Nearest Neighbors, KNN for short, is a supervised learning algorithm specializing in classification.
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The algorithm looks at different centroids and compares distance using some sort of function usually Euclidean , then analyzes those results and assigns each point to the group so that it is optimized to be placed with all the closest points to it. Random forests are a popular supervised ensemble learning algorithm.
In this case, the weak learners are all randomly implemented decision trees that are brought together to form the strong predictor — a random forest. Check out the video below for much more behind the scenes stuff regarding random forests. K-Means is a popular unsupervised learning classification algorithm typically used to address the clustering problem. The algorithm begins with randomly selected points and then optimizes the clusters using a distance formula to find the best grouping of data points.
You know the drill, check out the video for more. PCA is a dimensionality reduction algorithm that can do a couple of things for data scientists. Most importantly, it can dramatically reduce the computational footprint of a model when dealing with hundreds or thousands of different features. In a future version the app will offer you an option to sync your hitching spots with Hitchwiki with no extra work.
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