Most tabular datasets contain categorical features. The simplest way to work with these is to encode them with Label Encoder. It is simple, yet sometimes not accurate. Then I will show you how those could be improved through Single and Double Validation. The final part of the paper is devoted to the discussion of benchmarks results which also could be found in my GitHub repo — CategoricalEncodingBenchmark.

CatBoost: unbiased boosting with categorical features

The following material describes binary classification task, yet all formulas and approaches could be applied to multiclass classification as far as it could be represented as a binary classification and regression.

If you are looking for a better understanding of categorical encoding, I recommend you to grab a pen and some paper and make your own calculations with the formulas I provided below. The example train dataset looks like this:. The most common way to deal with categories is to simply map each category with a number. By applying such transformation, a model would treat categories as ordered integers, which in most cases is wrong.

If you are working with tabular data and your model is gradient boosting especially LightGBM libraryLE is the simplest and efficient way for you to work with categories in terms of memory the category type in python consumes much less memory than the object type.

The One Hot Encoding is another simple way to work with categorical columns. It takes a categorical column that has been Label Encoded and then splits the column into multiple columns.

The numbers are replaced by 1s and 0s depending on which column has what value. OHE expands the size of your dataset, which makes it memory-inefficient encoder. There are several strategies to overcome the memory problem with OHE, one of which is working with sparse not dense data representation.

Sum Encoder compares the mean of the dependent variable target for a given level of a categorical column to the overall mean of the target. However, the difference between them is the interpretation of LR coefficients: whereas in OHE model the intercept represents the mean for the baseline condition and coefficients represents simple effects the difference between one particular condition and the baselinein Sum Encoder model the intercept represents the grand mean across all conditions and the coefficients can be interpreted directly as the main effects.

Helmert coding is a third commonly used type of categorical encoding for regression along with OHE and Sum Encoding. It compares each level of a categorical variable to the mean of the subsequent levels. This type of encoding can be useful in certain situations where levels of the categorical variable are ordered, say, from lowest to highest, or from smallest to largest. You should think about it beforehand and make preprocessing of the train as close to the test as possible.

In such a case, Frequency Encoding would catch the similarity between rare columns. Target Encoding has probably become the most popular encoding type because of Kaggle competitions.If you have models that are trained with LightGBMVespa can import the models and use them directly.

This dumps the tree model and other useful data such as feature names, objective functions, and values of categorical features to a JSON file. An example of training and saving a model suitable for use in Vespa is as follows. To import the LightGBM model into Vespa, add the model file to the application package under a directory named modelsor a subdirectory under models.

Note that an application package can have multiple models. After putting the model somewhere under the models directory, it is then available for use in both ranking and stateless model evaluation. Vespa has a ranking feature called lightgbm. This ranking feature specifies the model to use in a ranking expression, relative under the models directory.

Main CV logic for LightGBM

Consider the following example:. One important issue to consider is how to map features in the model to features that are available for Vespa to use in ranking. When this model is evaluated in Vespa, Vespa expects that these feature names are valid rank features. You can also define functions which are valid rank features with the LightGBM feature name to perform the mapping. An example:. This allows dumping rank features from Vespa to train a model directly. For more information, see learning to rank.

If you have used XGBoost with Vespa previously, you might have noticed you have to wrap the xgboost feature in for instance a sigmoid function if using a binary classifier.

That should not be needed in LightGBM, as that information is passed along in the model dump as seen in the objective section in the JSON output above. Currently, Vespa supports regression, binary log loss and cross-entropy applications. Multi-class and ranking will soon be available. For more information on LightGBM and objective functions, see objective.

Here, the categorical feature is marked with the Pandas dtype category. This allows Vespa to extract the proper categorical values to use.

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To ensure that categorical variables are properly handled, construct training data based on Pandas tables and use the category dtype on categorical columns. In Vespa categorical features are strings, so mapping the above feature for instance to a document field would be:. Here, the string value of the document would be used as the feature value when evaluating this model for every document.

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LightGBM has support for categorical variables. I would like to know how it encodes them. It doesn't seem to be one hot encode since the algorithm is pretty fast I tried with data that took a lot of time to one hot encode.

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lightgbm categorical feature example

How are categorical features encoded in lightGBM? Ask Question. Asked 2 years ago. Active 8 months ago. Viewed 4k times. Skinishh Skinishh 49 1 1 silver badge 6 6 bronze badges. See this comment and the referenced issue: github. An update resource, for anyone interested in how lightgbm handles categorical features: Categorical feature support: lightgbm.

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If True, progress will be displayed at boosting stage. If an integer is given. Bases: lightgbm. LGBMModelobject. Create a callback that activates early stopping.

Activates early stopping. LightGBM latest. When data type is string, it represents the path of txt file label list or numpy 1-D arrayoptional — Label of the training data. Parameters: key str — The key to get attribute from. Returns: value — The attribute value of the key, returns None if attribute do not exist. Returns: result — Evaluation result list.

lightgbm categorical feature example

Returns: result — Array of feature names. Setting a value to None deletes an attribute. Parameters: params dict — Parameters for training. If callable, a custom evaluation metric, see note for more details. Returns: callback — The requested callback function. Return type: function lightgbm. Return type: function. If None, new figure and axes will be created. Pass None to disable. If None or smaller than 1, all features will be displayed. Only one metric supported because different metrics have various scales.

Pass None to pick first one according to dict hashcode. Pass None to plot all datasets. Returns: ax Return type: matplotlib Axes lightgbm. Returns: graph Return type: graphviz Digraph. This argument has highest priority over other data split arguments. If metrics is not None, the metric in params will be overridden.

Last entry in evaluation history is the one from best iteration. Results are not affected, and always contains std.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.

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My categorical features are encoded as integers. Here is an example:. Furthermore, libsvm represents all data values as floats.

Does LightGBM still correctly interpret the categorical columns as categorical and not numeric? Learn more. How to specify LightGBM categorical features when loading from libsvm format?

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Ask Question. Asked 1 month ago. Active 1 month ago. Viewed 16 times. Dataset ". Kevin Kevin 1. Active Oldest Votes. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password.

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Post as a guest Name.The data is stored in a Dataset object. LightGBM can use categorical features as input directly.

Note:You should convert your categorical features to int type before you construct Dataset.

lightgbm categorical feature example

And you can use Dataset. If you concern about your memory consumption. You can save memory accroding to following:.

LightGBM can use either a list of pairs or a dictionary to set parameters. For instance:. If you have a validation set, you can use early stopping to find the optimal number of boosting rounds. The model will train until the validation score stops improving.

If early stopping occurs, the model will have an additional field: bst. Note that train will return a model from the last iteration, not the best one. This works with both metrics to minimize L2, log loss, etc. Note that if you specify more than one evaluation metric, all of them will be used for early stopping. If early stopping is enabled during training, you can get predictions from the best iteration with bst. LightGBM latest. Install python-package dependencies, setuptoolsnumpy and scipy is required, scikit-learn is required for sklearn interface and recommended.

Run: pip install setuptools numpy scipy scikit - learn - U. Dataset 'train. To load a scpiy. Dataset csr.

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Dataset 'test.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service. Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.

It only takes a minute to sign up. After searching extensively I am unable to find a detail explanation and clear example of how exhaustive bipartition search is used by LightGBM light gradient boosting as an 'optimal' solution for handling categorical data. There are also discussions on Githut e. It is not easy to track down this information. In the case of regression and binary classification, it can be shown that after ordering the data, the binary splits will occur between contiguous blocks.

Let us consider the case of binary classification. Say you have 5 categories with proportion of 1's given by. Normally, you would have to check every possible partition into 2 subsets exponential. However, the theorem tells you that you can first order according to proportion of 1's.

That is what is meant by contiguous, and it has to do with an information theoretic argument related to the centroid the value minimizing the expected loss for a leaf in the tree. The paper provides a necessary condition, but for particular cases like l2 norm regression and binary classificationit leads to the above result. Sign up to join this community.

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Viewed 19 times. Particularly for high-cardinality categorical features, a tree built on one-hot features tends to be unbalanced and needs to grow very deep to achieve good accuracy. Instead of one-hot encoding, the optimal solution is to split on a categorical feature by partitioning its categories into 2 subsets. But there is an efficient solution for regression trees[8].

The basic idea is to sort the categories according to the training objective at each split. Is anyone please able to explain: By way of example how this bipartition search works in particular e.