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Gradient Boosting in Machine Learning
Another very popular Boosting algorithm is Gradient Boosting. Just like AdaBoost,Gradient Boosting works by sequentially adding predictors to an ensemble, each one correcting its predecessor. However, instead of tweaking the instance weights at every iteration like AdaBoost does, this method tries to fit the new predictor to the residual errors made by the previous predictor.… Read more
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Boosting( Ensemble) Trees | Machine Learning from Scratch
Introduction Boosting (originally called hypothesis boosting) refers to any Ensemble method that can combine several weak learners into a strong learner. The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor. There are many boosting methods available, but by far the most popular are AdaBoost13 (short for… Read more
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Charlie has a Magic Mirror Program in Java
Charlie possesses an extraordinary tool in his possession: a magic mirror capable of displaying right-rotated versions of any given word. This mirror offers a unique perspective, revealing fascinating transformations of words. In this blog post, we’ll delve into the Java code that produces these right-rotations, exploring how Charlie’s mirror brings about these linguistic metamorphoses. Also… Read more
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Angry Professor | Hackerrank Solution
A Discrete Mathematics professor has a class of students. Frustrated with their lack of discipline, the professor decides to cancel class if fewer than some number of students are present when class starts. Arrival times go from on time (arrivalTime<=0) to arrived late (arrivalTime>0). Given the arrival time of each student and a threshhold number… Read more
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Sending Data in Unstructured File Form
Unstructured data files consist of a series of bits. The file doesn’t separate the bits from each other in any way. You can’t simply look into the file and see any structure because there isn’t any to see. Unstructured file formats rely on the file user to know how to interpret the data. For example,… Read more
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Random Forests | Machine Learning from Scratch
As we have discussed, a Random Forest is an ensemble of Decision Trees, generally trained via the bagging method (or sometimes pasting), typically with max_samples set to the size of the training set. Instead of building a BaggingClassifier and passing it a DecisionTreeClassifier, you can instead use the RandomForestClassifier class, which is more convenient and… Read more