d3 is winner.

No

Feature magnitude matters because: The regression coefficients of linear models are directly influenced by the scale of the variable. Variables with bigger magnitude / larger value range dominate over those with smaller magnitude / value range Gradient descent converges faster when features are on similar scales Feature scaling helps decrease the time to find support vectors for SVMs Euclidean distances are sensitive to feature magnitude. Some algorithms, like PCA require the features to be centered at 0. The machine learning models affected by the feature scale are: Linear and Logistic Regression Neural Networks Support Vector Machines KNN K-means clustering Linear Discriminant Analysis (LDA) Principal Component Analysis (PCA) Feature Scaling Feature scaling refers to the methods or techniques used to normalize the range of independent variables in our data, or in other words, the methods to set the feature value range within a similar scale. Feature scaling is generally the last step in the data preprocessing pipeline, performed just before training the machine learning algorithms. There are several Feature Scaling techniques, which we will discuss throughout this section: Standardisation Mean normalisation Scaling to minimum and maximum values - MinMaxScaling Scaling to maximum value - MaxAbsScaling Scaling to quantiles and median - RobustScaling Normalization to vector unit length

https://githubhelp/sandy4321/feature-scaling-1

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