Questions tagged [feature-scaling]
51 questions
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How to implement the Feature Scaling Technique with StandardScalar to multiple columns in machine learning?
We need to predict the Credit Card Approvals using various Machine Learning methods.
Credit_card_label
Credit_card
import pandas as pd
import numpy as np
ccard = pd.read_csv("Credit_card.csv")
ccard_label =…
Yogesh Navandhar
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Do i need to use RobustScaler() and OneHotEncoder() in new data before model.predict()
Suppose I have this dataframe (in a regression problem) with numerical and categorical data:
df_example
Var1_numerical Var2_categorical Var3_numerical Var4_categorical Var_to_predict
20 …
Daniel_DS
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Does it makes sense to scale features by only one label before using logistic regression?
I have a simple binary classification problem, my current classifier is Logistic Regression and I'm using RobustScaler from sklearn to scale my features before fitting the lr.
Assuming my features are looking like 2 Gaussians:
While the orange…
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1 answer
Should we always first perform feature normalization and then the feature reduction?
Sometimes performing feature reduction reduces number of features with methods like PCA and then we could scale only the relevant variables. Is there a rule that we need to do normalization/scaling first and then the feature reduction?
Sharat Ainapur
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Unable to inverse_transform the value of feature because of different dimensionality
I'm designing a multivariate time series model. For that I'm inputing 5 features to lstm model and try to predict the output of 1 variable(i.e. whose value is dependent on itself and other 4 features).
For that I'm doing the feature scaling as…
Siddharth Bhatheja
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Is there any data scaling methods except for Min-Max Normalization and Quantile transformation that keeps the range between [0,1]?
I have been working on a machine learning project and I have been trying to scale the features before feeding it to my model. I know Min-Max Normalization and Quantile Transformation scales down the features in the range of 0 and 1. I was wondering…
Jacob Issac
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