{"id":6917,"date":"2023-07-24T15:57:35","date_gmt":"2023-07-24T23:57:35","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=6917"},"modified":"2025-04-24T17:15:06","modified_gmt":"2025-04-24T17:15:06","slug":"random-forest-regression-in-python-using-scikit-learn","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/random-forest-regression-in-python-using-scikit-learn\/","title":{"rendered":"Random Forest Regression in Python Using Scikit-Learn"},"content":{"rendered":"\n\n\n\n\n
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Photo by Lukasz Szmigiel<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n<\/div>\n
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Introduction<\/h1>\n

A random forest is an ensemble model that consists of many decision trees<\/a>. Predictions are made by averaging the predictions of each decision tree. Or, to extend the analogy\u2014much like a forest is a collection of trees, the random forest model is also a collection of decision tree models. This makes random forests a strong modeling technique that\u2019s much more powerful than a single decision tree.<\/p>\n

Each tree in a random forest is trained on the subset of data provided. The subset is obtained both with respect to rows and columns. This means each random forest tree is trained on a random data point sample, while at each decision node, a random set of features is considered for splitting.<\/p>\n

In the realm of machine learning, the random forest regression algorithm can be more suitable for regression problems than other common and popular algorithms. Below are a few cases where you\u2019d likely prefer a random forest algorithm over other regression algorithms:<\/p>\n

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  1. There are non-linear or complex relationships between features and labels.<\/li>\n
  2. You need a model that\u2019s robust, meaning its dependence on the noise in the training set is limited. The random forest algorithm is more robust than a single decision tree, as it uses a set of uncorrelated decision trees.<\/li>\n
  3. If your other linear model implementations are suffering from overfitting, you may want to use a random forest.<\/li>\n<\/ol>\n

    A bit more about point 3. Decision trees are prone to overfitting, especially if we don\u2019t limit the max depth\u2014they have unlimited flexibility, and hence can keep growing until they have exactly one leaf node for every single data point, thus perfectly predicting all of them.<\/p>\n

    When we limit the max depth of a decision tree, the variance is reduced, but bias increases. To keep both variance and bias low, the random forest algorithm combines many decision trees with randomness to reduce overfitting.<\/p>\n

    Before we jump into an implementation of random forest for a regression problem, let\u2019s define some key terms.<\/p>\n

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