Questions tagged [normalization]

The tag has no usage guidance, but it has a tag wiki.

Filter by
Sorted by
Tagged with
1
vote
0answers
8 views

Sliding Window Normalization

I've been reading about time series forecasting and many approaches use a sliding window method. What I don't get about it is how to properly normalize your data. Most codes I've seen so far normalize ...
0
votes
1answer
12 views

Normalized 2D tensor values are not in range 0-1

Below function takes in 2D tensor and normalizes it using broadcasting .The issue is except all values to be in range 0-1 but the result has values outside this range . How to get all values in 2D ...
1
vote
1answer
46 views

What is the difference between normalization and re-scaling?

This site does not describe the nature of the normalization tag. How does it differ from re-scaling? Many authors use the two terms interchangeably. I can not understand normalization's operational ...
0
votes
0answers
26 views

Considerations to take into account when clustering

My idea is to use clustering to perform stock segmentation based on risk, building different risk levels that might adapt better to different kind of users. Hence I have computed different risk ...
1
vote
1answer
29 views

Why does my manual derivative of Layer Normalization imply no gradient flow?

I recently tried computing the derivative of the layer norm function (https://arxiv.org/abs/1607.06450), an essential component of transformers, but the result suggests that no gradient flows through ...
1
vote
0answers
14 views

Derivative of the function of random variable

Suppose we have a function $\phi(X)$ of random variable $X$. Suppose both of $\phi(X)$ and $X$ are random variables. If $\phi$ is differentiable, how to calculate the derivative of $\phi(X)$ w.r.t. $...
0
votes
0answers
30 views

Why does normalization improve my decision tree performances?

I have a regression problem for which I have to try several models, so I normalized my data and then tried to use a decision tree regressor (from sklearn.tree) and I noticed very good results (...
0
votes
1answer
23 views

Normalization for a 2d input array

I am new to machine learning and trying to apply it to my problem. I have a training dataset with 44000 rows of features with shape 6, 25. I want to build a sequential model. I was wondering if there ...
3
votes
2answers
75 views

Layer normalization details in GPT-2

I've read that GPT-2 and other transformers use layer normalization before the self-attention and feedforward blocks, but I am still unsure exactly how the normalization works. Let's say that our ...
1
vote
0answers
25 views

Normalizing variables with logarithmic shape

A simple model with two variables [A,B] to train, let's say, a logistic regression or any other classification model: A: Flat distribution from 0 to 100. B: A logarithmic distribution from 0 to a few ...
1
vote
0answers
37 views

whether to use normalization while performing PCA?

I have an excel file containing a table where I registered the frequency of three linguistic phenomena in 72 poems. Since the poems have different lengths I normalized the results dividing each value ...
0
votes
0answers
5 views

Encoding Data and huge loss during ANN training

I just started to learn on ANN and tried to experiment on my own on a Linear Regression. I got a dataset which had housing prices for a city. Tried going through this but my model gives me a huge loss....
0
votes
0answers
13 views

estimate user's satisfaction of a video based on how much of it they watched - normalization

I am trying to estimate how much a user liked a video using how much of the video they watched. Let's say, on the scale of 1 to 10, 1 means that the user didn't like it at all, and 10 means they ...
0
votes
0answers
4 views

While Merging image datasets which of the image parameters should be prepossessed/Normalized before giving to a CNN Model?

When two datasets are merged or images of different parameters size, dimension, Format are combine which parameters of the datasets should be normalized/ pre-processed before giving it to a model?
0
votes
1answer
22 views

Cleaning NaNs with averages pre or post split? [duplicate]

I have a column with some NaNs in it and I want to replace those NaNs with the average/median/mode. Technically, the validation/ test data has never been seen before - so how could I include it in the ...
0
votes
0answers
13 views

Is normalizing term weight necessary when cosine similarity is used in retrieval?

When using cosine similarity in information retrieval, document vector length and query vector length are used for normalization. So if TF-IDF is used as a weighting function, then using raw frequency ...
0
votes
0answers
31 views

Scalling features for competition participants

Hello there and Happy Holidays. I have a data set with each row representing a race with 6 participants, with each participant having its own column for each feature. The target variable is ...
0
votes
0answers
17 views

Normalization of encoded feature?

I am a beginner in ML, and I am working on a classification problem on big data (its shape is (8921483, 52)) which its features are mostly categorical. One of the features has 175365 different ...
1
vote
1answer
31 views

First perform data augmentation or normalization?

Should I first perform data augmentation or normalization in deep learning? I am mainly interested in 2D and 3D input data. In tutorials that I have seen so far the data augmentation always comes ...
0
votes
0answers
17 views

How to standardize or normalize in neurons

Question Please help understand where and how to do standardization (convert values in-between -1 and 1) at a neuron and the rationals, or please correct if my understanding is wrong. I am assuming ...
1
vote
0answers
15 views

Rank advertisement and Score keywords used in advertisement based on its performance

I am trying to classify my advertisements and trying to understand which advertisement is performing better than others and why. Step - 1: compare two ads based on CTR. The problem here is I am ...
2
votes
1answer
35 views

What parameters to use when normalising training, validation, and testing data?

I know a similar post was made here, but I wanted to ask some follow up questions. I am conducting a cross-validation search to find values of a set of hyper-parameters and need to normalise the data. ...
0
votes
1answer
13 views

Scaling the data iteratively one by one vs batch scaling

I have 2000 signals in a dataset of shape (2000, 400000) where each signal is recorded within the range -127, 128. I want to downscale each signal from (-127, 128) ...
0
votes
0answers
13 views

Relationship between gradient norm and layer depth

I was wondering, if there was some relationship between the gradient norms of a layer in a neural network and its respective depth. I would suspect the: The smaller the depth of the layer, the bigger ...
2
votes
1answer
22 views

Is feature scaling at all needed for a feature set with a single feature?

I understand that feature scaling is required to bring features in different magnitudes on a common scale so the model is not biased towards features with higher magnitudes. But if there is only a ...
0
votes
1answer
39 views

How to choose between different types of feature scaling?

The feature set for my multi-class multi-label classification task, using the MLPClassifier from scikit learn, contains mostly features where the values are in the same range of [0,1], but there are 3 ...
0
votes
2answers
31 views

What is the best way to treat datetime in the preprocessing step of machine learning

I have two datetime columns in my dataset. What I have done so far I have extracted year, ...
1
vote
1answer
42 views

Why standard distribution for ML [closed]

Data normalization: It ensures that each input (each pixel value, in this case) comes from a standard distribution. This standardization makes our model train and reach a minimum error, faster! my ...
0
votes
1answer
34 views

How to normalize the data correctly in spam dataset

I'm working on the spam dataset to classify the inputs into binary classes. my problem is that: the observations in the dataset are floats small numbers in the first 53 column, and the 54 is float ...
0
votes
1answer
42 views

Scaling of variables considering the values of a single column or the whole dataset

I read many time that for machine learning and data mining algorithms the multi-dimensional input data should be scaled (e.g. normalized or standardized). Now my question is whether the average, min ...
2
votes
1answer
107 views

How does layer normalization work exactly?

As far as I understand, layer normalization normalizes across all the features for fully connected layers. Does that mean that for each batch dimension we have to learn the normalization parameters? ...
1
vote
1answer
28 views

difference between scaling/normalizing data at a specific step

I am using the MinMaxScaler normalization method, however I have seen various ways that this can be done, I want to know if there is any actual difference between the following: 1. Standardizing/...
1
vote
0answers
26 views

Generalize min-max scaling to vectors

I am combining several vectors, where each vector is a certain kind of embedding of some object. Since each embedding is very different (some have all components between $[0, 1]$ some have components ...
0
votes
1answer
32 views

What is an example of normalization (making the row unit norm)?

I am learning about standardization and normalization concepts for feature engineering. Standardization is done for example using z-score where based on the mean and std deviation we re-calculate the ...
0
votes
1answer
46 views

How to normalize noisy data

Suppose I have 1-D data which has some outliers, I want to normalize the data to be in the range [0,1]. I tried calculating the maximum value and the minimum value as follows: ...
2
votes
2answers
112 views

Should normalization be applied?

I have more then 100 columns with the values of 1-0. But the two features at the end as seen in the below image, have different values then the rest. Should I rescale the values in the last two ...
0
votes
1answer
35 views

How to use a multiple linear regression model built from normalized data

I built a linear multivariable regression model from normalized data (for the interval [0; 1]). Initially, the data was not normalized, I normalized the data by myself (independent and dependent ...
1
vote
0answers
27 views

Normalisation of features extracted from audio files

I am building CNN and SVM models which take in MFCC features as input. The MFCC matrices shape is (13, n). The 13 rows are coefficients and n columns represent n time frames. So each row in the matrix ...
0
votes
1answer
38 views

Normalize data with extreme outliers for forecasting

Suppose I have input values that represent the change of a stock share from each time step to the next. Now I want to feed these values into an LSTM Neural Net. My problem is that most values are ...
0
votes
2answers
22 views

Selecting Transforms with sklearn pipelines

So I am currently working on a Data set, and I want to use Pipelines to select the transforms. Here is an example of what I want to do : ...
1
vote
1answer
44 views

Logistic Regression from scratch in numpy - Is data normalization needed?

I was trying to implement Logistic Regression from scratch in python to learn better how it works under the hood. In particular I am following this video tutorial from Andrew Ng. This is the dataset I ...
0
votes
0answers
32 views

Performance worsen when normalizing on CNN

I am facing quite a strange behaviour. As far as I have understood, especially when dealing with CNN, feature normalisation/ standardisation, should help the model at converging faster. Now, I am ...
0
votes
0answers
27 views

Neural network output is 0 for test data (using RELU for activation)

Maybe this is a naive question, but I have a NN that uses relu for all layers. In train data there is no problem, but in test (or validation) the outputs are all 0. ...
0
votes
0answers
13 views

Does it make sense to train kalman filter on unscaled (Timeseries-)data, to clean it? [duplicate]

I have Timeseries data to clean and I got the tip to use a kalman Filter. My Question is, does it make sense to "fit" this kalman-filters parameters to an unscaled and unnormalized (...
2
votes
2answers
61 views

What is the best way to normalize a set of datasets

I have a data set that contains the same Time series "Sensor readings" for different days and I want to make a deep learning model to predict these values. What I did was I splatted the data ...
4
votes
1answer
275 views

feature scaling xgbRegressor

I read for example in this answer: Does the performance of GBM methods profit from feature scaling? that scaling doesn´t affect the performance of any tree-based method, not for lightgbm,xgboost,...
1
vote
1answer
78 views

Generating the right target for an LSTM model

Trying to explain my question on a simplified data set. Having the following dataset: ...
0
votes
1answer
107 views

Use both differencing and normalization in time series modeling to make it stationary?

I am working on a time series dataset. Should we use both differencing and normalizing or either of the ones to make it stationary?
0
votes
0answers
26 views

Understanding Scaling With Multiple Datatypes in a Neural Network

I've looked around and seen a huge amount of discussion on scaling inputs and targets for neural networks, but can't seem to find universal agreement on a few issues. Suppose I had a dataset, with ...
1
vote
0answers
15 views

Normalization around the nominal value while avoiding to get too big results

Lets say we measured a signal N times. Each measurement contains T time instance. For example for N=4 measurements and T=5 time instances the data matrix is as follows: ...

1
2 3 4 5