Linked Questions

3
votes
1answer
112 views

Why does standardizing my data cause better results? [duplicate]

I've trained a simple Neural net (scikit learn's MLPClassifier) in order to perform binary classification on some data (the titanic dummy problem on kaggle). I know that standardizing data prior to ...
0
votes
1answer
38 views

Why 0 mean is desirable for data in neural networks? [duplicate]

It is suggested to normalize data as 0 mean and 1 variance. Also, TanH considered better than Sigmoid activation function as it has 0 mean. Why 0 mean is important?
0
votes
0answers
22 views

Effect of learning rate (and standardization) on NN with ReLu layers [duplicate]

I'm trying to understand the effect of the learning rate on a 10x10x10x10x4 sequential NN. Where each hidden layer is ReLu and the output layer being Softmax. I know the theory: low rate -> slow ...
44
votes
2answers
47k views

Are mean normalization and feature scaling needed for k-means clustering?

What are the best (recommended) pre-processing steps before performing k-means?
17
votes
3answers
21k views

What algorithms need feature scaling, beside from SVM?

I am working with many algorithms: RandomForest, DecisionTrees, NaiveBayes, SVM (kernel=linear and rbf), KNN, LDA and XGBoost. All of them were pretty fast except for SVM. That is when I got to know ...
10
votes
1answer
12k views

Decision trees variable (feature) scaling and variable (feature) normalization (tuning) required in which implementations?

In many machine learning algorithms, feature scaling (aka variable scaling, normalization) is a common prepocessing step Wikipedia - Feature Scaling -- this question was close Question#41704 - How and ...
2
votes
1answer
6k views

Normalize row or column while each row is an observation

Suppose I have a matrix compose of row as each observation, column as each property and I want to calculate the distance between each observation. In this case I think I should normalize each column, ...
6
votes
1answer
507 views

Are there any theoretically rigorous justification for why scaling or normalizing data should improve statistical performance?

I was wondering if there was a rigorous theoretical justification for why normalizing (or scaling) the feature vectors might make sense? The reason that I am asking this is because, at least in the ...
0
votes
1answer
3k views

Normalizing (mean and std) a 3D array

I am having troubles visualizing how to normalize a 3D matrix. I am trying to use the spectrogram of sound files for a sound classification task using neural networks. This is how a spectrogram looks ...
1
vote
1answer
1k views

Neural Net for multivariate regression

I need to build a model (M) that converts a 10 dimensional space of inputs (A) into a 20 dimensional space of outputs B. Both the inputs and outputs are analog, so this is not a classification ...
0
votes
1answer
289 views

Why does Feature Scaling work?

If I take a very basic example where my feature Matrix X is $$ \begin{matrix} 1 & 100 & 0.25\\ 1 & 110 & 0.5\\ 1 & 120 & 0.75\\ 1 & 130 & 1\\ 1 & 140 & 1.25\\ \...
0
votes
2answers
262 views

Does feature standardization always make sense?

I wonder if feature scaling like this makes always sense for neural networks: Let $T$ be the training set and $x_i \in \mathbb{R}^n$ with $d_i \in T$ be the feature vector of $d_i$. Then add another ...