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Decision Tree Learning


Ensemble Modeling

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In the world of analytics,modeling is a general term used to refer to the use of data mining (machine learning) methods to develop predictions. If you want to know what ad a particular user is more likely to click on, or which customers are likely to leave you for a competitor, you develop a predictive model. There are a lot of models to choose from: Regression, Decision Trees, K Nearest Neighbor, Neural Nets, etc. They all will provide you with a prediction, but some will do better than others depending on the data you are working with. While there are certain tricks and tweaks one can do to improve the accuracy of these models, it never hurts to remember the fact that there is wisdom to be found in the masses.


The New Machine Learning Specialization : in-depth review

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The lectures starts with defining the decision trees, the splitting criteria,and different uses of the tree like applying the algorithm to categorial features, splitting on continuous features,or using the trees for regression problems, then it explains combining multiple trees and using Ensemble Learning to apply Random Forest, in the last lecture we take a glimpse of XGBoost and how to use them, without any more details. This is probably the most hyped part of the whole specialization, I found many people celebrating that this introductory course will discuss such topics.


Machine Learning Pipelines

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In this use case, we will be using the Titanic dataset. In this dataset, we will apply some common Transformers on certain columns and then we will use a Decision Tree Estimator to classify whether the passenger will live or die. Here is the plan outline for our use case. To make our use case easy to understand, let us see the diagram below. This will give you a fairly good understanding of the pipeline visually.


Estimating a Book's Publication Date with Artificial Intelligence

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You're probably aware of AI's increasing ability to analyze and synthesize human language, such as the recent controversy over whether a Google chatbot is, in fact, sentient (Google claims -- and I'm inclined to believe -- that the chatbot is just very, very good at recognizing and replicating speech patterns). Since AI is so skilled at analyzing language, I wondered whether it could detect changes in language over time. Could it differentiate between texts written in, say, the 12th century and the 18th century? As it turns out, it can! To build this model, I used natural language processing, the branch of machine learning dedicated to (you guessed it!)


Introducing random forests in R

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In this post, I will present how to use random forests in classification, a prediction technique consisting in generating a set of trees (hence, a forest) bootstrapping the features used in each tree. We do this to obtain trees that are not necessarily using the strongest predictors at the beginning. I will test this technique in a LoanDefaults dataset to predict which customers will default the paying of a loan in a specific month. This dataset has two interesting features: the number of positive cases is much smaller than the negatives and requires some preprocessing of the existing features. I will be using the ranger (RANdom forest GEneRator) package, skimr to get a summary of data, rpart and rpart.plot to generate an alternative decision tree model, BAdatasets to access the dataset, tidymodels for prediction workflow facilities and forcats for the variable importance plot.


Random Forest,GBM(Gradient Boosting Machines)

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In this article, I will talk about Random forest and GBM methods and their properties. The decision of making strategic splits heavily affects a tree's accuracy. The decision criteria is different for classification and regression trees. Decision trees use multiple algorithms to decide to split a node in two or more sub-nodes. The creation of sub-nodes increases the homogeneity of resultant sub-nodes.


Can ML predict where my cat is now?

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With months of historic location and temperature data captured, this blog covers how to train a machine learning (ML) model to predict where my cat would go throughout her day. For the impatient, you can skip directly to the prediction web-app here. With some inexpensive hardware (and a cat ambivalent to data privacy concerns) I wanted to see if I could train a machine learning (ML) model to predict where Snowy the cat would go throughout her day. Home based location and temperature tracking allowed me to build up an extensive history of which room she used for her favourite sleeping spots. I had a theory that with sufficient data collected from her past I'd be able to train an ML model to predict where she was likely to go in the future.


What is the Random forest algorithm?

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Random Forest is a supervised machine learning algorithm that is widely and comprehensively used in classification and regression problems. It builds decision trees on different samples and takes a majority vote for classification and the mean in the regression case. The term "Random Forest Classifier" refers to a classification algorithm made up of several multiple decision trees. A stochastic algorithm is used to build each tree individually to enhance non-correlated forests, which then uses predictive forest powers to make highly accurate decisions. Here we can use the random forest algorithm for both classifications and regression tasks.


Classifying Human Traffic with Random Forest Decision Trees

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Classifying Human Traffic with Random Forest Decision Trees At bitly, we study human behavior on the social web, and we often need to figure out when data is generated by a deliberate human action...


Resources Center

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Decision trees are a popular intuitive supervised machine learning algorithm, that is part of the sklearn library, and has wide areas of applications like- business growth opportunities evaluation, demographic-driven data client targeting, and strategic management planning. Every machine learner worth their salt needs to familiarize themselves with the decision trees machine learning model. These free machine learning with random forests and decision trees pdf course notes will teach you how do decision trees work, how they ensemble into the random forest algorithm, what are their pros and cons, which are the most commonly used performance metrics and much more.