Decision Trees<\/a>, Random Forests<\/a> and Boosting<\/a>\u00a0are among the top 16 data science and machine learning tools<\/a> used by data scientists. The three methods are similar, with a significant amount of overlap. In a nutshell:<\/p>\n Decision trees<\/strong> are a series of sequential steps designed to answer a question and provide probabilities, costs, or other consequence of making a particular decision.<\/p>\n <\/a>They are simple to understand, providing a clear visual to guide the decision making progress. However, this simplicity comes with a few serious disadvantages<\/em>, including overfitting<\/a>,\u00a0error due to bias<\/a>\u00a0and error due to variance<\/a>.\u00a0\u00a0<\/span><\/p>\n As noted above, decision trees are fraught with problems. A tree generated from 99 data points might differ significantly from a tree generated with just one different data point. If there was a way to generate a very large number of trees, averaging out their solutions, then you’ll likely get an answer that is going to be very close to the true answer. Enter the\u00a0random forest<\/strong>\u2014a collection of decision trees with a single, aggregated result. Random forests\u00a0are commonly reported as the most accurate learning algorithm.\u00a0<\/p>\n Random forests reduce the variance seen in decision trees by:<\/p>\n A single decision tree is a weak predictor, but is relatively fast to build. More trees give you a more robust<\/a>\u00a0model and prevent overfitting. However, the more trees you have, the slower the process.<\/strong> Each tree in the forest has to be generated, processed, and analyzed. In addition, the more features you have, the slower the process (which can sometimes take hours<\/a>\u00a0or even days<\/a>); Reducing the set of features can dramatically speed up the process.<\/p>\n Another distinct difference between a decision tree and random forest is that while a decision tree is easy to read\u2014you just follow the path and find a result\u2014a random forest is a tad more complicated to interpret.<\/strong> There are a slew of articles out there designed to help you read the results from random forests (like this one<\/a>), but in comparison to decision trees, the learning curve is steep.<\/p>\n Like random forests, gradient boosting<\/a> is a set of decision trees. The two main differences are:<\/p>\n If you carefully tune parameters, gradient boosting can result in better performance<\/strong> than random forests. However, gradient boosting may not be a good choice if you have a lot of noise<\/strong>, as it can result in overfitting. They also tend to be harder to tune<\/strong> than random forests.<\/span><\/p>\n Random forests and gradient boosting each excel in different areas. Random forests perform well for\u00a0multi-class object detection<\/a>\u00a0and bioinformatics,<\/a>\u00a0which tends to have a lot of statistical noise. Gradient Boosting performs well when you have unbalanced data such as in real time risk assessment.<\/a><\/span><\/p>\n Top Data Science and Machine Learning Methods Used in 2017<\/a><\/p>\n Random Forests explained intuitively<\/a><\/p>\n Boosting Algorithms for Better Predictions<\/a><\/p>\n Overfitting in Decision Trees<\/a><\/p>\n Gentle Introduction to the Bias-Variance Trade-Off in Machine Learning<\/a><\/p>\n How to improve random Forest performance?<\/a><\/p>\n Training a Random Forest with a big dataset seems very slow #257<\/a><\/p>\n Random Forests<\/a><\/p>\n Gradient Boosting vs Random Forests<\/a><\/p>\n Multi-class object detection<\/a><\/span><\/p>\n Using random forest for reliable classification and cost-sensitive learning for medical diagnosis<\/a><\/span><\/p>\n Applications of Gradient Boosting Machines<\/a><\/span><\/p>\n A Gentle Introduction to Gradient Boosting<\/a><\/span><\/p>\n Application of Stochastic Gradient Boosting (SGB) Technique to Enhance the Reliability of Real-Time Risk Assessment Using AVI and RTMS Data<\/a><\/span><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"\n
Decision Trees and Their Problems<\/h2>\n
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Random Forest vs Decision Trees<\/h2>\n
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Random Forest vs Gradient Boosting<\/h2>\n
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References<\/h2>\n