Do I have to do normalization on my data if all the features are of the same scale? for example, all the columns are features and each row/sample is the number of occurrences for each feature? And if normalization is required do I need feature-wise or sample-wise normalization?
When to perform Normalization or Standardization in machine learning?
562 views Asked by Martina Morcos At
1
There are 1 answers
Related Questions in MACHINE-LEARNING
- Trained ML model with the camera module is not giving predictions
- Keras similarity calculation. Enumerating distance between two tensors, which indicates as lists
- How to get content of BLOCK types LAYOUT_TITLE, LAYOUT_SECTION_HEADER and LAYOUT_xx in Textract
- How to predict input parameters from target parameter in a machine learning model?
- The training accuracy and the validation accuracy curves are almost parallel to each other. Is the model overfitting?
- ImportError: cannot import name 'HuggingFaceInferenceAPI' from 'llama_index.llms' (unknown location)
- Which library can replace causal_conv1d in machine learning programming?
- Fine-Tuning Large Language Model on PDFs containing Text and Images
- Sketch Guided Text to Image Generation
- My ICNN doesn't seem to work for any n_hidden
- Optuna Hyperband Algorithm Not Following Expected Model Training Scheme
- How can I resolve this error and work smoothly in deep learning?
- ModuleNotFoundError: No module named 'llama_index.node_parser'
- Difference between model.evaluate and metrics.accuracy_score
- Give Bert an input and ask him to predict. In this input, can Bert apply the first word prediction result to all subsequent predictions?
Related Questions in NEURAL-NETWORK
- Influence of Unused FFN on Model Accuracy in PyTorch
- How to train a model with CSV files of multiple patients?
- Does tensorflow have a way of calculating input importance for simple neural networks
- My ICNN doesn't seem to work for any n_hidden
- a problem for save and load a pytorch model
- config QConfig in pytorch QAT
- How can I convert a flax.linen.Module to a torch.nn.Module?
- Spiking neural network on FPGA
- Error while loading .keras model: Layer node index out of bounds
- Matrix multiplication issue in a Bidirectional LSTM Model
- Recommended way to use Gymnasium with neural networks to avoid overheads in model.fit and model.predict
- Loss is not changing. Its remaining constant
- Relationship Between Neural Network Distances and Performance
- Mapping a higher dimension tensor into a lower one: (B, F, D) -> (B, F-n, D) in PyTorch
- jax: How do we solve the error: pmap was requested to map its argument along axis 0, which implies that its rank should be at least 1, but is only 0?
Related Questions in DATASET
- How to add a new variable to xarray.Dataset in Python with same time,lat,lon dimensions with assign?
- Power BI Automations of Audits and APIs
- Trouble understanding how to use list of String data in a Machine Learning dataset - Features expanded before making prediction
- how to difference values within several panels
- How to use an imported Excel file inside Anylogic model
- Need to be able to load different reports into the same report viewer, based on the selection of a combobox value How do i do this?
- Can i merge my custom model and pretrained model in yolov9
- How to access the whole public dataset hosted on a website?
- Use dataset name in knitr code chunk in R
- How many images should I label from the training set?
- How to get a list of numbers out of an awk output in bash
- Wrong file reading in Jupyter
- Request for Rui Li twitter dataset
- Illustrator file to single word Dataset
- Image augmentation for dataset creation
Related Questions in NORMALIZATION
- Threshold scaling along a straight line
- How to Normalize a function in python?
- Feature Scaling with MinMaxScaler()
- Min-max scaling on DCT coefficients
- Swift Image preprocessing: normalization with mean [0.485, 0.456, 0.405] std [0.229, 0.224, 0.225]
- Divide two signal stream using GNU Radio but no result appear
- Why does the Min-Max normalization produces inaccurate results when used in dtype='<i2' in python
- Should I turn my skewed data into a normal distributed data before using MinMaxScaler or StandardScaler?
- How to get the message being passed in torch geometric?
- Data Normalisation in transformation then Batch Normalisation in ResNet50 pytorch
- Finding standard deviation and mean for Normalize function from torchvision
- Can't normalize my custom index to start at 0% y-intercept
- the prediction results are so far from the original data that the new information cannot be used, is there something wrong?
- Normalizing the numerical values
- Min-Max Normalization by group across multiple columns
Related Questions in STANDARDIZATION
- Pytorch Normalize() receiving torch.float32 tensor but recognising it as torch.int32
- KeyError on indexing a dataframe on an index that should exist
- Standardisation Across Different Datasets Before Lasso Regression
- Convert spectrum to Color (Python)
- How to calculate % improvement with differing denominators?
- standardization of all variables LASSO/OLS
- How to apply standardization for train set inside gridsearchcv?
- Do Stata's lasso commands automatically standardize dummy variables?
- Pipeline including StandardScaler and Gaussian Naive Bayes with low accuracy
- How to standardize data with different dilution factors and minimum values for each dilution factor?
- Do you need to standardize WoE-encoded variables when using L1 regularized Logistic Regression?
- Data standardisation from a dataset in matlab, why do i have this error?
- Do we need to exclude OneHotEncoded columns while standardizing or normalizing using MinMaxScaler() or StandardScaler()?
- Is there any method to standarize time series data?
- What is the name of the table operation that standardizes the template structure?
Popular Questions
- How do I undo the most recent local commits in Git?
- How can I remove a specific item from an array in JavaScript?
- How do I delete a Git branch locally and remotely?
- Find all files containing a specific text (string) on Linux?
- How do I revert a Git repository to a previous commit?
- How do I create an HTML button that acts like a link?
- How do I check out a remote Git branch?
- How do I force "git pull" to overwrite local files?
- How do I list all files of a directory?
- How to check whether a string contains a substring in JavaScript?
- How do I redirect to another webpage?
- How can I iterate over rows in a Pandas DataFrame?
- How do I convert a String to an int in Java?
- Does Python have a string 'contains' substring method?
- How do I check if a string contains a specific word?
Trending Questions
- UIImageView Frame Doesn't Reflect Constraints
- Is it possible to use adb commands to click on a view by finding its ID?
- How to create a new web character symbol recognizable by html/javascript?
- Why isn't my CSS3 animation smooth in Google Chrome (but very smooth on other browsers)?
- Heap Gives Page Fault
- Connect ffmpeg to Visual Studio 2008
- Both Object- and ValueAnimator jumps when Duration is set above API LvL 24
- How to avoid default initialization of objects in std::vector?
- second argument of the command line arguments in a format other than char** argv or char* argv[]
- How to improve efficiency of algorithm which generates next lexicographic permutation?
- Navigating to the another actvity app getting crash in android
- How to read the particular message format in android and store in sqlite database?
- Resetting inventory status after order is cancelled
- Efficiently compute powers of X in SSE/AVX
- Insert into an external database using ajax and php : POST 500 (Internal Server Error)
No, you do not have to do normalization on your data if all your features are on the same scale.
For standardization, you want to check the statistical distribution of your data to make sure they have a standard normal distribution with mean,μ=0 and standard deviation, σ=1; where μ is the mean (average) and σ is the standard deviation from the mean.
You can do this in pandas by calling
.describe()on your data and investigating themeanandstd. If it happens that some features have normal distribution while others don't, you can carry-our sample-wise standardization (on the entire dataset).