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Decision Trees are versatile Machine Learning algorithms that can perform both classification and regression tasks,and even multioutput tasks. Decision Trees are the fundamental components of Random Forests,which are among the most powerful Machine L…
This is the 2nd part of the series. Read the first part here: Logistic Regression Vs Decision Trees Vs SVM: Part I In this part we’ll discuss how to choose between Logistic Regression , Decision Trees and Support Vector Machines. The most correct ans…
Classification is one of the major problems that we solve while working on standard business problems across industries. In this article we’ll be discussing the major three of the many techniques used for the same, Logistic Regression, Decision Trees…
Machine Learning Methods: Decision trees and forests This post contains our crib notes on the basics of decision trees and forests. We first discuss the construction of individual trees, and then introduce random and boosted forests. We also discuss…
What are the advantages of logistic regression over decision trees?FAQ The answer to "Should I ever use learning algorithm (a) over learning algorithm (b)" will pretty much always be yes. Different learning algorithms make different assumptions…
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if you aggregate the predictions of a group of predictors,you will often get better predictions than with the best individual predictor. a group of predictors is called an ensemble:this technique is called Ensemble Learning,and an Ensemble Learning a…
属性与特征: attribute: e.g., 'Mileage' feature: an attribute plus its value, e.g., 'Mileage = 15000' Note that some regression algorithm can be used for classification as well,and vice versa. For example,Logistic Regression is commonly used for classifica…
xgboost的可以参考:https://xgboost.readthedocs.io/en/latest/gpu/index.html 整体看加速5-6倍的样子. Gradient Boosting, Decision Trees and XGBoost with CUDA By Rory Mitchell | September 11, 2017  Tags: CUDA, Gradient Boosting, machine learning and AI, XGBoost   Gradie…
the main steps: 1. look at the big picture 2. get the data 3. discover and visualize the data to gain insights 4. prepare the data for machine learning algorithms 5. select a model and train it 6. fine-tune your model 7. present your solution 8. laun…