Stack Overflow
Stack Overflow

Questions tagged [one-hot-encoding]

One-Hot Encoding is a method to encode categorical variables to numerical data that Machine Learning algorithms can deal with. One-Hot encoding is most used during feature engineering for a ML Model. It converts categorical values into a new categorical column and assign a binary value of 1 or 0 to those columns.

Also known as Dummy Encoding, One-Hot Encoding is a method to encode categorical variables, where no such ordinal relationship exists, to numerical data that Machine Learning algorithms can deal with. One hot encoding is the most widespread approach, and it works very well unless your categorical variable takes on a large number of unique values. One hot encoding creates new, binary columns, indicating the presence of each possible value from the original data. These columns store ones and zeros for each row, indicating the categorical value of that row.

1224 questions
11
votes
2 answers

Explain onehotencoder using python

I am new to scikit-learn library and have been trying to play with it for prediction of stock prices. I was going through its documentation and got stuck at the part where they explain OneHotEncoder(). Here is the code that they have used : >>> from…
Shashwat Siddhant
  • 411
  • 2
  • 5
  • 17
11
votes
1 answer

Chisel: how to implement a one-hot mux that is efficient?

I have a table, where each row of the table contains state (registers). There is logic that chooses one particular row. Only one row receives the "selected" signal. State from that chosen row is then accessed. Either a portion of the state is…
seanhalle
  • 973
  • 7
  • 27
10
votes
1 answer

XGBoost error - When categorical type is supplied, DMatrix parameter `enable_categorical` must be set to `True`

I have four categorial features and a fifth numerical one (Var5). When I try the following code: cat_attribs = ['var1','var2','var3','var4'] full_pipeline = ColumnTransformer([('cat', OneHotEncoder(handle_unknown = 'ignore'), cat_attribs)],…
tewerty
  • 367
  • 1
  • 3
  • 9
10
votes
3 answers

How do I resolve one hot encoding if my test data has missing values in a col?

For example if my training data has the categorical values (1,2,3,4,5) in the col,then one hot encoding will give me 5 cols. But in the test data I have, say only 4 out of the 5 values i.e.(1,3,4,5).So one hot encoding will give me only 4…
Nikhil Mishra
  • 1,182
  • 2
  • 18
  • 34
10
votes
2 answers

In Torch how do I create a 1-hot tensor from a list of integer labels?

I have a byte tensor of integer class labels, e.g. from the MNIST data set. 1 7 5 [torch.ByteTensor of size 3] How do use it to create a tensor of 1-hot vectors? 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0…
W.P. McNeill
  • 16,336
  • 12
  • 75
  • 111
9
votes
2 answers

How to convert one-hot vector to label index and back in Pytorch?

How to transform vectors of labels to one-hot encoding and back in Pytorch? The solution to the question was copied to here after having to go through the entire forum discussion, instead of just finding an easy one from googling.
Gulzar
  • 23,452
  • 27
  • 113
  • 201
9
votes
4 answers

Converting a Pandas Dataframe column into one hot labels

I have a pandas dataframe similar to this: Col1 ABC 0 XYZ A 1 XYZ B 2 XYZ C By using the pandas get_dummies() function on column ABC, I can get this: Col1 A B C 0 XYZ 1 0 0 1 XYZ 0 1 0 2 XYZ 0 0 1 While…
Nir_J
  • 133
  • 1
  • 3
  • 7
9
votes
1 answer

Tensorflow confusion matrix using one-hot code

I have multi-class classification using RNN and here is my main code for RNN: def RNN(x, weights, biases): x = tf.unstack(x, input_size, 1) lstm_cell = rnn.BasicLSTMCell(num_unit, forget_bias=1.0, state_is_tuple=True) stacked_lstm =…
SON.PARK
  • 111
  • 1
  • 6
9
votes
3 answers

How to generate one hot encoding for DNA sequences?

I would like to generate one hot encoding for a set of DNA sequences. For example the sequence ACGTCCA can be represented as below in a transpose manner. But the code below will generate the one hot encoding in horizontal way in which I would prefer…
Xiong89
  • 767
  • 2
  • 13
  • 24
8
votes
2 answers

Is it possible to specify handle_unknown = 'ignore' for certain columns and 'error' for others inside OneHotEncoder?

I have a dataframe with all categorical columns which i am encoding using a oneHotEncoder from sklearn.preprocessing. My code is as below: from sklearn.preprocessing import OneHotEncoder from sklearn.pipeline import Pipeline steps =…
sayo
  • 207
  • 4
  • 18
8
votes
2 answers

keras to_categorical adds additional value

I have 4 classes which I need to predict, am using keras' to_categorical to achieve that, I expected to get a 4 one-hot-encoded array, but it seems I get 5 values instead, an additional [0] value appears for all rows dict = {'word': 1,…
Exorcismus
  • 2,243
  • 1
  • 35
  • 68
8
votes
1 answer

How do I "one hot encode" a Tensorflow Dataset?

Newby here... I loaded TF dataset as follows: dataset = tf.data.TFRecordDataset(files) dataset.map(extract_fn) The dataset contains a "string column" with some values and I want to "one-hot" encode them. I could do that in the extract_fn record by…
Marsellus Wallace
  • 17,991
  • 25
  • 90
  • 154
8
votes
4 answers

Standardization before or after categorical encoding?

I'm working on a regression algorithm, in this case k-NearestNeighbors to predict a certain price of a product. So I have a Training set which has only one categorical feature with 4 possible values. I've dealt with it using a one-to-k categorical…
Franch
  • 621
  • 4
  • 9
  • 22
8
votes
2 answers

How to make onehotencoder in Spark to work like onehotencoder in Pandas?

When I use onehotencoder in Spark,I will get the result as in fourth column which is a sparse vector. // +---+--------+-------------+-------------+ // | id|category|categoryIndex| categoryVec| // +---+--------+-------------+-------------+ // | 0| …
Mohamed Ibrahim
  • 191
  • 1
  • 12
8
votes
1 answer

How can I use categorical one-hot labels for training with Keras?

I have inputs that look like this: [ [1, 2, 3] [4, 5, 6] [7, 8, 9] ...] of shape (1, num_samples, num_features), and labels that look like this: [ [0, 1] [1, 0] [1, 0] ...] of shape (1, num_samples, 2). However, when I try to run the following…
Ivan Vegner
  • 1,707
  • 4
  • 14
  • 23
1 2
3
…
81 82