I am unsure but probably it's because you can infer the remaining class. For instance, imagine you have two colors {red, blue} and you want encode that variable. One option is to create two columns, one for red and other for blue, but you could also create just n - 1 columns for example "red"; if the value is 1 then the sample is red otherwise is blue.
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I am unsure but probably it's because you can infer the remaining class. For instance, imagine you have two colors {red, blue} and you want encode that variable. One option is to create two columns, one for red and other for blue, but you could also create just n - 1 columns for example "red"; if the value is 1 then the sample is red otherwise is blue.
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If you review the output of the encoding, you are correct that there are 11 columns. However, only 10 of these columns contain new information that is based on a comparison between the categories in the carrier column. The 11th column, "intercept", is simply a series of 1s and will have no impact on your modelling.
As to why this 11th column exists, I'm not completely sure. Following the logic of the backward difference encoding table, perhaps it could be viewed as comparing the uppermost level with the next level up. Given that every value in the column is belongs to the uppermost level or below, all values for the encoded column in this comparison will be 1 (and the other values, if they existed, would be 0).
Scikit-learn
contrib.scikit-learn.org › category_encoders › backward_difference.html
Backward Difference Coding — Category Encoders 2.8.1 documentation
class category_encoders.backward_difference.BackwardDifferenceEncoder(verbose=0, cols=None, mapping=None, drop_invariant=False, return_df=True, handle_unknown='value', handle_missing='value')[source]
GitHub
github.com › scikit-learn-contrib › category_encoders › blob › master › category_encoders › backward_difference.py
category_encoders/category_encoders/backward_difference.py at master · scikit-learn-contrib/category_encoders
class BackwardDifferenceEncoder(BaseContrastEncoder): """Backward difference contrast coding for encoding categorical variables. · Parameters · ---------- · verbose: int · integer indicating verbosity of the output. 0 for none. cols: list ·
Author: scikit-learn-contrib
Scikit-learn
contrib.scikit-learn.org › category_encoders › _modules › category_encoders › backward_difference.html
category_encoders.backward_difference — Category Encoders 2.2.2 documentation
[docs]class BackwardDifferenceEncoder(BaseEstimator, TransformerMixin): """Backward difference contrast coding for encoding categorical variables. Parameters ---------- verbose: int integer indicating verbosity of the output. 0 for none. cols: list a list of columns to encode, if None, all ...
Hdi-project
hdi-project.github.io › ballet › _modules › category_encoders › backward_difference.html
category_encoders.backward_difference — ballet 0.7.11 documentation
[docs]class BackwardDifferenceEncoder(BaseEstimator, TransformerMixin): """Backward difference contrast coding for encoding categorical variables. Parameters ---------- verbose: int integer indicating verbosity of the output. 0 for none. cols: list a list of columns to encode, if None, all ...
Scikit-learn
contrib.scikit-learn.org › category_encoders
Category Encoders — Category Encoders 2.11.1 documentation
import category_encoders as ce encoder = ce.BackwardDifferenceEncoder(cols=[...]) encoder = ce.BaseNEncoder(cols=[...]) encoder = ce.BinaryEncoder(cols=[...]) encoder = ce.CatBoostEncoder(cols=[...]) encoder = ce.CountEncoder(cols=[...]) encoder = ce.CountTargetEncoder(cols=[...]) encoder = ...
PyPI
pypi.org › project › category-encoders › 1.2.0
category-encoders · PyPI
encoder = ce.BackwardDifferenceEncoder(cols=[…]) encoder = ce.BinaryEncoder(cols=[…]) encoder = ce.HashingEncoder(cols=[…]) encoder = ce.HelmertEncoder(cols=[…]) encoder = ce.OneHotEncoder(cols=[…]) encoder = ce.OrdinalEncoder(cols=[…]) encoder = ce.SumEncoder(cols=[…]) encoder = ce.PolynomialEncoder(cols=[…])
Scikit-learn
contrib.scikit-learn.org › category_encoders › index.html
Category Encoders — Category Encoders 2.8.1 documentation
import category_encoders as ce encoder = ce.BackwardDifferenceEncoder(cols=[...]) encoder = ce.BaseNEncoder(cols=[...]) encoder = ce.BinaryEncoder(cols=[...]) encoder = ce.CatBoostEncoder(cols=[...]) encoder = ce.CountEncoder(cols=[...]) encoder = ce.GLMMEncoder(cols=[...]) encoder = ...
GitHub
github.com › pheman › categorical-encoding
GitHub - pheman/categorical-encoding: A library of sklearn compatible categorical variable encoders
import category_encoders as ce encoder = ce.BackwardDifferenceEncoder(cols=[...]) encoder = ce.BinaryEncoder(cols=[...]) encoder = ce.HashingEncoder(cols=[...]) encoder = ce.HelmertEncoder(cols=[...]) encoder = ce.OneHotEncoder(cols=[...]) encoder = ce.OrdinalEncoder(cols=[...]) encoder = ...
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PyPI
pypi.org › project › featurewiz
featurewiz · PyPI
BackwardDifferenceEncoder: BackwardDifferenceEncoder is a Backward difference contrast coding for encoding categorical variables.
Practical Business Python
pbpython.com › categorical-encoding.html
Guide to Encoding Categorical Values in Python - Practical Business Python
import category_encoders as ce # Get a new clean dataframe obj_df = df.select_dtypes(include=['object']).copy() # Specify the columns to encode then fit and transform encoder = ce.BackwardDifferenceEncoder(cols=["engine_type"]) encoder.fit_transform(obj_df, verbose=1).iloc[:,8:14].head()
GitHub
github.com › Mapleleaff › categorical-encoding
GitHub - Mapleleaff/categorical-encoding: A library of sklearn compatible categorical variable encoders
import category_encoders as ce encoder = ce.BackwardDifferenceEncoder(cols=[...]) encoder = ce.BinaryEncoder(cols=[...]) encoder = ce.HashingEncoder(cols=[...]) encoder = ce.HelmertEncoder(cols=[...]) encoder = ce.OneHotEncoder(cols=[...]) encoder = ce.OrdinalEncoder(cols=[...]) encoder = ce.SumEncoder(cols=[...]) encoder = ce.PolynomialEncoder(cols=[...]) encoder = ce.BaseNEncoder(cols=[...]) encoder = ce.LeaveOneOutEncoder(cols=[...])
Author: Mapleleaff
Insaid
community.insaid.co › hc › en-us › community › posts › 360042568353-Backward-Difference-Encoding
Backward Difference Encoding – International School of AI & Data Science
March 30, 2020 - Could you please help me with advanced label Encoding techniques like Backward Difference encoding
GitHub
github.com › scikit-learn-contrib › category_encoders › issues › 125
Backward difference encoding changes DataFrame column names · Issue #125 · scikit-learn-contrib/category_encoders
September 21, 2018 - This might be a quick one as it looks like it's intentional, I just don't understand why. I tried a BackwardDifferenceEncoder and found that it prepended all of my columns with "col_&q...
Author: scikit-learn-contrib
Kaggle
kaggle.com › harishvutukuri › categorical-variable-encoding
Categorical Variable Encoding | Kaggle
February 13, 2020 - Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources
Stack Exchange
stats.stackexchange.com › questions › 419990 › logic-of-forward-or-backward-difference-coding
categorical encoding - Logic of forward or backward difference coding - Cross Validated
July 31, 2019 - Two systems of contrast coding for ordinal data are forward difference coding and backward difference coding. I will focus on the latter system here because it seems to be more commonly used, but my
LinkedIn
linkedin.com › pulse › encode-categorical-features-revanth-yadama
Encode-Categorical-Features
October 12, 2021 - Handling Categorical/Qualitative variables is an important step in data pre-processing. Many Machine learning algorithms can not understand categorical variables by themselves unless we convert them to numerical values.
Dive into Deep Learning
d2l.ai › chapter_recurrent-modern › seq2seq.html
10.7. Sequence-to-Sequence Learning for Machine Translation — Dive into Deep Learning 1.0.3 documentation
In so-called sequence-to-sequence problems such as machine translation (as discussed in Section 10.5), where inputs and outputs each consist of variable-length unaligned sequences, we generally rely on encoder–decoder architectures (Section 10.6). In this section, we will demonstrate the ...
Medium
chrisyandata.medium.com › understanding-cross-encoders-architecture-implementation-and-applications-d70e6fcba240
Understanding Cross-Encoders: Architecture, Implementation, and Applications | by Chris Yan | Medium
November 6, 2024 - Understanding Cross-Encoders: Architecture, Implementation, and Applications Cross-encoders are a powerful class of models widely used in tasks that require precise pairwise scoring, such as …
Medium
medium.com › @kakumar1611 › the-illustrated-guide-to-cross-encoders-from-deep-to-shallow-2a23a8630016
The Illustrated Guide to Cross-Encoders: From Deep to Shallow | by Kapil Kumar | Medium
December 18, 2024 - The Illustrated Guide to Cross-Encoders: From Deep to Shallow This blog explores cross-encoders, their functionality, strengths, and trade-offs in modern information retrieval systems like Retrieval …