Question

I have query in machine Learning in Python.Can someone explain me the difference between Random Oversampling,Smote,XGBoost,Random Forest Classifier,FFT in Python.Visual representation would be much helpful.Can someone guide me?

Answer 1

RANDOM SAMPLING:- Suppose you have data with two labels toxic comments and non-toxic comments. Now one of the labels has a lot fewer data points for example data has 50000 rows where non-toxic comments are 40000 and toxic comments only 10000. Hence to deal with this data imbalance we generally duplicate the examples from minority label and add them to the training dataset. This is called Random Sampling.

Smote:- again smote is used to handle class imbalance. In smote we draw a line between the minority class and synthetically add new data points around those lines.

XGBoost :-

Gradient boosting is a method that goes through cycles to iteratively add models into an ensemble.

It begins by initializing the ensemble with a single model, whose predictions can be pretty naive. (Even if its predictions are wildly inaccurate, subsequent additions to the ensemble will address those errors.)

Then, we start the cycle:

• First, we use the current ensemble to generate predictions for each observation in the dataset. To make a prediction, we add the predictions from all models in the ensemble.
• These predictions are used to calculate a loss function (like mean squared error, for instance).
• Then, we use the loss function to fit a new model that will be added to the ensemble. Specifically, we determine model parameters so that adding this new model to the ensemble will reduce the loss. (Side note: The "gradient" in "gradient boosting" refers to the fact that we'll use gradient descent on the loss function to determine the parameters in this new model.)
• Finally, we add the new model to the ensemble, and ...
• ... repeat!

Random Forest Classifier : A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.

fft :- It is an algorithm that plays a very important role in the computation of the Discrete Fourier Transform of a sequence. It converts a space or time signal to signal of the frequency domain.

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