And when not to use XGBoost?Two common terms used in ML is Bagging & BoostingBagging: It is an approach where…
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Just like how we train a neural network before using it for making predictions we have to train (build) a…
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By Preet Gandhi, NYUIntroduction In the era of data science, artificial intelligence is making impossible feats possible. Driverless cars, IBM…
Continue ReadingA Complete View of Decision Trees and SVM in Machine Learning
Let’s assume our data has p inputs and a response for each of N observations. To construct a regression tree:Consider…
Continue ReadingClassification using Decision Trees
IntroductionData Scientists use machine learning techniques to make predictions under a variety of scenarios. Machine learning can be used to…
Continue ReadingBasic Ensemble Learning (Random Forest, AdaBoost, Gradient Boosting)- Step by Step Explained
Remember, boosting model’s key is learning from the previous mistakes.Gradient Boosting learns from the mistake — residual error directly, rather than update…
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Let’s imagine that we make decisions 10% slower so in addition to an unhappy inbox, we end up making only…
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Discretization methods fall into 2 categories: supervised and unsupervised.Unsupervised methods do not use any information, other than the variable distribution,…
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Similarly, a true negative is an outcome where the model correctly predicts the negative class.False Positive & False NegativeThe terms False…
Continue ReadingAn Implementation and Explanation of the Random Forest in Python
What this means is the decision tree tries to form nodes containing a high proportion of samples (data points) from…
Continue ReadingA Guide to Decision Trees for Machine Learning and Data Science
A Guide to Decision Trees for Machine Learning and Data ScienceGeorge SeifBlockedUnblockFollowFollowingNov 30Decision Trees are a class of very powerful Machine…
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