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Do you need to scale target variable?
Yes, you do need to scale the target variable. I will quote this reference: A target variable with a large spread of values, in turn, may result in large error gradient values causing weight values to change dramatically, making the learning process unstable.
How do you change a target variable?
Manually transform the target variable. Automatically transform the target variable….It involves the following steps:
- Create the transform object, e.g. a MinMaxScaler.
- Fit the transform on the training dataset.
- Apply the transform to the train and test datasets.
- Invert the transform on any predictions made.
How many categorical features are in the dataset?
The dataset consists of 7 categorical features which includes one text description feature. From above image it is understood that the target variable is having 15 classes and also the dataset is imbalanced. Let’s begin with the process of developing a text classification model.
How to standardize the data of different scales?
When you’re doing data analysis, you might find yourself with a number of different variables to work with. For example, perhaps you have invited participants to your lab and run them through your experiment.
Why is it necessary to scale the target?
The functional form is the same but the learning rate for the second coordinate has to be adjusted to 1/1000 of that for the first coordinate to match it. If not coordinate two will dominate and the Δ vector will point more towards that direction. As a result it biases the delta to point across that direction only and makes the converge slower.
When do you need to standardize your dataset?
Standardization is useful when your data has varying scales and the algorithm you are using does make assumptions about your data having a Gaussian distribution, such as linear regression, logistic regression, and linear discriminant analysis. Dataset: I have used the Lending Club Loan Dataset from Kaggle to demonstrate examples in this article.