How to implement normalization in Python and R?
Implement normalization in Python and R
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Implement normalization in Python and R
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One-hot encoding is used to transform the categorical variables into dummy variables which is used in order to fit in the model.
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Normalization refers to rescaling real valued numeric attributes into the range 0 and 1.
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Precision and recall are two of the evaluation techniques used in a machine learning model which tells us how better a model can perform. But there…
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In loan approval, precision can be dangerous because it considers false positive rate when a person is not eligible for a loan but a machine predicts…
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For removing unnamed columns, we need to import the dataset and after importing following libraries
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In logistic regression, p-value can be found by the following code
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ROC curve can give us a clear idea to set a threshold value to classify the label and also help in model optimization.
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F1-Score is the harmonic mean of the precision and recall. Multiplying the constant of 2 scales the score to 1 when both precision and recall are 1.
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Following are the evaluation parameters considered in logistic regression
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Grid search is an algorithm used for tuning a model by considering every hyperparameters specified and used for evaluating a model. It is used to…
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Cross-validation is a method which is used to estimate the performance of a model.K- Fold is used to evaluate the model on a limited data sample.
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