11 votes
Accepted

What is the difference between one-hot and dummy encoding?

Most machine learning models accept only numerical variables. This is the reason behind why categorical variables are converted to number so the model can understand better. Now lets address your ...
Archana David's user avatar
8 votes

What is the difference between one-hot and dummy encoding?

The purpose of one-hot encoding is to assign numbers to categorical variables which does not create a false, meaningless numerical pattern. If you have categorical variables "Apple", "...
Misha Lavrov's user avatar
8 votes
Accepted

How to handle categorical variables with Random Forest using Scikit Learn?

Is one-hot encoding an option? It seems like no, due to the high cardinality of your feature, it might result in the course of dimensionality problems if your sample size is small and also if you are ...
Multivac's user avatar
  • 2,969
6 votes

Difference between tf.keras.backend.one_hot and keras.utils.to_categorical

The difference is the input and output. The utils.to_categorical function takes a vector as input and returns a matrix with one hot encoded rows. The ...
mahesh ghanta's user avatar
6 votes
Accepted

Scikit-learn OneHotEncoder effect on feature selection

Yes it would be possible that it happens. It means that this event happening has no importance for the target. Imagine a categorical feature with a lot of categories(high cardinality). Maybe only one ...
Carlos Mougan's user avatar
6 votes

What is the difference between one-hot and dummy encoding?

To complete Archana David's answer: From what I encountered, the big advantage of sklearn.preprocessing.OneHotEncoder is that you can save it as an scikit-learn ...
Adept's user avatar
  • 874
5 votes
Accepted

One Hot Encoding for any kind of dataset

I would recommend to use the one hot encoding package from category encoders and select the columns you want to using pandas select dtypes. ...
Carlos Mougan's user avatar
5 votes

On gradient boosting and types of encodings

This is actually a feature of tree-based models in general, not just gradient boosting trees. Not exactly a reference, but this Medium article explains why ordinal encoding is often more efficient. On ...
zachdj's user avatar
  • 2,684
5 votes

How do I encode the categorical columns if there are more than 15 unique values?

If you have high cardinality categorical data(+10 distinct values) you can do Target Encoding. One hot Encoding in high cardinality scenarios has the following drawbacks: The input data for the model ...
Carlos Mougan's user avatar
5 votes

How do I encode the categorical columns if there are more than 15 unique values?

While most answers here suggest to use various encoding schemes, I would like to propose a different approach: collapsing categories. The idea is that if there are two (or more) similar categories, ...
Yossi Levy's user avatar
5 votes
Accepted

Multiple classes present in one-hot encoding

Yes, it is possible to do it exactly as you describe it. This is called multilabel-classification. If doing so, you would treat each element of the output as an independent prediction of a binary ...
Broele's user avatar
  • 1,362
4 votes

Possible harm in standardizing one-hot encoded features

With unpenalized linear models, there is no difference. The coefficients will just scale to counteract the new scale of the variables, and the intercept will shift to compensate for the centering. ...
Ben Reiniger's user avatar
  • 11.8k
4 votes

How do I encode the categorical columns if there are more than 15 unique values?

[edit] See also Carlos' answer, I think it's better than mine. You should use one hot encoding for the categorical features. Replacing categorical values with numerical ones would be a bad idea, ...
Erwan's user avatar
  • 25.4k
4 votes

How do I encode the categorical columns if there are more than 15 unique values?

You are right that most of the algorithms can digest only numerical data, i.e. the categorical features need to be converted to the numerical ones before running the regression. Besides ...
aivanov's user avatar
  • 1,510
4 votes
Accepted

Does One-Hot encoding increase the dimensionality and sparsity of dataset?

Which encoding technique to use depends on your data/features. Ordinal encoding is used when there ia a sense of order in your feature. For example you have a feature performance which has values ...
spectre's user avatar
  • 2,075
4 votes

How to handle categorical variables with Random Forest using Scikit Learn?

One-hot encoding (OHE) is the standard method to represent a categorical feature. In my opinion 200 is not high dimensionality, it's very common to use OHE on text data with a much higher number of ...
Erwan's user avatar
  • 25.4k
3 votes

If a categorical feature only occurs a few times in a data set, should I drop it?

Indeed it's often a good idea to remove boolean features which are very rare, but the problem is that choosing a threshold by intuition is not necessarily optimal. Whenever possible the optimal value ...
Erwan's user avatar
  • 25.4k
3 votes
Accepted

Isn't one-hot encoding a waste of information?

Good idea but... You encode not just to transform from categorical to numerical features but to give that information to your model. Let's say that you do that and feed it through a linear model to ...
Carlos Mougan's user avatar
3 votes
Accepted

Random Forrest Sklearn gives different accuracy for different target label encoding with same input features

Yes. With y being a 1d array of integers (as after LabelEncoder), sklearn treats it as a multiclass classification problem. With y being a 2d binary array (as after LabelBinarizer), sklearn treats it ...
Ben Reiniger's user avatar
  • 11.8k
3 votes

What are the sparse and dense vector ? I cant undestand ,can you explain to me?please.Why do we use for?

A sparse matrix A sparse matrix is a matrix that is comprised of mostly zero values. e.g 1, 0, 0, 1, 0, 0 A = 0, 0, 2, 0, 0, 1 0, 0, 0, 2, 0, 0 A ...
Saurabh Kansal's user avatar
3 votes
Accepted

Does Fasttext use One Hot Encoding?

A quick answer would be No. Let's walk through how FastText works internally: For representation purposes, FastText internally initializes a dictionary. Dictionary contains all the collection of ...
Vikas Bhandary's user avatar
3 votes
Accepted

When to One-Hot encode categorical data when following Crisp-DM

To me it depends, because I would separate some types of Categorical Variables : Categorical variables with few classes : OneHot as fast as you can Categorical variable with some highly-represented ...
Adept's user avatar
  • 874
3 votes

Treating missing data in categorical features

You could break the column 2 from your example into number of columns : Image,Video.... So the new features will be like: ...
Shiv's user avatar
  • 679
3 votes

How to deal with Different Shapes of X_train and X_test after OneHotEncoding?

The issue that you are running into is because you are using the fit_transform method on both your training and test dataset. The correct way of using a transformer ...
Oxbowerce's user avatar
  • 7,507
3 votes

Does One-Hot encoding increase the dimensionality and sparsity of dataset?

What you describe is ordinal encoding. If there is an inherent order to your data (such as age), you can definitely try it. And yes, one-hot encoding does increase dimensionality and sparsity of the ...
serali's user avatar
  • 1,242
3 votes
Accepted

Confusion regarding One Hot Encoding

You're confusing binary encoding with one hot encoding (OHE): there's no reason to interpret the array of 3 binary variables [0,1,0] as a single binary number. The 3 values are interpreted by any ML ...
Erwan's user avatar
  • 25.4k
3 votes
Accepted

Why does SciKit-Learn's OneHotEncoder take so long on a Large Dataset?

If you use OneHotEncoder in a Jupyter Notebook, you can use %%prun -s "time" to profile your code. See How do I ...
Connor's user avatar
  • 631
3 votes

Beginner basic clustering model and one-hot encoding?

Once you are done fitting the model, you can label each of your records based on the cluster. df['cluster_labels'] = kmeans.labels_ For ease of analysis, you can ...
Kriti's user avatar
  • 363
2 votes
Accepted

Encoding features in sklearn

LabelEncoder converts strings to integers, but you have integers already. Thus, LabelEncoder will not help you anyway. Wenn you are using your column with integers ...
lanenok's user avatar
  • 1,516
2 votes
Accepted

Dropping one category for regularized linear models

When you do linear regression you have to leave out one column as it's a singular matrix and hence columns are linearly dependent and we cannot calculate the inverse. But when you do regularization it ...
prashant0598's user avatar
  • 1,501

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