# Lightgbm regression

Events and Time are critical variables of this kind of approach. The events are considered extreme situations that may occur in every moment: classical examples are churns, deaths, faults and so on. The definition of time is simple but a little bit abstract.

Traditionally, survival analysis was developed to measure lifespans of individuals. All we need to define is the Events and Time. In this post, we develop a solution to predict how many daily bike rentals are registered under some kind of temporal and weather conditions.

The task is in the format of a regression problem where we have to predict a count, this can also remind us a Poisson regression problem where we have to model a discrete variable. When Time and Event are defined i. The Survival function S is a function of the time which defines the probability the death event has not occurred yet at time t, or equivalently, gives us the proportion of the population with the time to event value more than t.

The Hazard function H is the rate at which the event is taking place. Given a fixed time interval, it is a measure of risk: the greater the hazard the greater the risk of failure. There are various approaches to fit survival models. In detail, we are interested in the so-called semi-parametric approachwhere we try to learn the hazard function with a very elegant and straightforward trick: we assume to may subdivide time into reasonably small intervals, i.

In this format, a piece-wise proportional hazards model is equivalent to a certain Poisson regression model. The fact that Survival function can be derived from the Hazard function and vice versa, is particularly useful because permits us to switch easily our target according to our interests. The dataset we utilize is related to the two-year historical data corresponding to years and from the Capital Bikeshare system of Washington D.

At the moment we consider the daily data focusing our attention in predicting the count of casual users reached at the end of the day. Various predictors are available to produce forecasts like temporal or weather regressors. Learning a Hazard function applying the semi-parametric exponential approach is quite easy with a LGBM regressor. This is possible as introduced above because the negative log-likelihood of the survival problem is 1-to-1 mapped with the negative log-likelihood of a Poisson regression, which is by default initialized in the LGBM library.

All we need to do is arrange our data for this purpose. Particularly, the dataset has first to be extended: each line is duplicated in multiple lines from 0 to casual count registered which is our death Event.

Then two new columns are generated:. With our expanded dataset we are ready to start training. The fit is computed as always: our target is represented by score a 0—1 variable and our regressors are all the available external variables plus count so farpreviously generated by ourselves. All the magic is made by our assumptions and by the Poisson loss. In the phase of prediction, we have to manipulate our test data as made before, expanding the input data with the creation of the count so far variable score is unknown.

Remember also that the LGBM predicts the Hazard function, to obtain the corresponding Survival function we have to operate this simple post-process transformation: 1-exp cumsum H.Parameters can be set both in config file and command line. By using config files, one line can only contain one parameter.

You can use to comment. If one parameter appears in both command line and config file, LightGBM will use the parameter from the command line. LightGBM supports continued training with initial scores.

It uses an additional file to store these initial scores, like the following:. It means the initial score of the first data row is 0. The initial score file corresponds with data file line by line, and has per score per line. And if the name of data file is train. In this case, LightGBM will auto load initial score file if it exists. LightGBM supports weighted training.

It uses an additional file to store weight data, like the following:. It means the weight of the first data row is 1. The weight file corresponds with data file line by line, and has per weight per line.

In this case, LightGBM will load the weight file automatically if it exists. Also, you can include weight column in your data file. For learning to rank, it needs query information for training data. LightGBM uses an additional file to store query data, like the following:. It means first 27 lines samples belong to one query and next 18 lines belong to another, and so on.

If the name of data file is train. In this case, LightGBM will load the query file automatically if it exists.

LightGBM latest. It might be useful, e. This is used to deal with over-fitting when data is small. For example, if you set it to 0. It does not slow the library at all, but over-constrains the predictions intermediatea more advanced methodwhich may slow the library very slightly. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter if 0.

MaxValue Note : using large values could be memory consuming. For example, the gain of label 2 is 3 in case of default label gains separate by. It uses an additional file to store these initial scores, like the following: 0. It uses an additional file to store weight data, like the following: 1. LightGBM uses an additional file to store query data, like the following: 27 18 By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

Gradient Boost Part 1: Regression Main Ideas

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. I want to do a cross validation for LightGBM model with lgb. The following approach works without a problem with XGBoost's xgboost. The task is to do regression, but the following code throws an error: Supported target types are: 'binary', 'multiclass'.

Got 'continuous' instead. By default, the stratify parameter in the lightgbm. According to the documentation :. But stratify works only with classification problems. So to work with regression, you need to make it False. Learn more. Python: LightGBM cross validation.

How to use lightgbm. Ask Question. Asked 2 years ago. Active 2 years ago. Viewed 16k times. Marius Marius 1 1 gold badge 2 2 silver badges 8 8 bronze badges. Active Oldest Votes. Vivek Kumar Vivek Kumar Looks like it was swapped to True here. One to keep in mind for the future! Thanks, this is very strange that stratified is True by default, because you can't run a regression.

But now it works! But if I do that with lgb. Why it didn't return mean absolute error? If I understand correctly l1 stands for lasso regression regularization? Marius, L1 norm and MAE are the same thing. See the LightGBM docs or the scikit-learn docs. Thanks Stev for clarifying that! Sign up or log in Sign up using Google. Sign up using Facebook.

Asked 2 years, 2 months ago. Active 2 years, 2 months ago. Viewed 1k times.

### Gradient Boosting with Scikit-Learn, XGBoost, LightGBM, and CatBoost

I am new to lightgbm package I am trying to build linear regression model with following sample train data having medianhousevalue as response variable in rstudio housingMedianAge totalRooms totalBedrooms population households medianIncome medianHouseValue 41 8.

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Technical site integration observational experiment live on Stack Overflow. Dark Mode Beta - help us root out low-contrast and un-converted bits. Related Hot Network Questions.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. It's quite clear for me what L2-regularization does in linear regression but I couldn't find any information about its use in LightGBM. It does basicly the same.

Regularization term again is simply the sum of the Frobenius norm of weights over all samples multiplied by the regularization parameter lambda and divided by the number of samples. You add this to the cost function of the machine learning algorithm that you work on just like linear regression. Just imagine or express manually that you add the regularization term to the cost function just like it is done on the cost function of the linear regression.

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Question feed. Cross Validated works best with JavaScript enabled.LightGBM latest. Source code for lightgbm.

Note, that the usage of all these parameters will result in poor estimates of the individual class probabilities. If None, all classes are supposed to have weight one. Minimum loss reduction required to make a further partition on a leaf node of the tree.

Subsample ratio of the training instance.

## XGBOOST vs LightGBM: Which algorithm wins the race !!!

Subsample ratio of columns when constructing each tree. L1 regularization term on weights. L2 regularization term on weights.

If 'split', result contains numbers of times the feature is used in a model. If 'gain', result contains total gains of splits which use the feature. Returns params : dict Parameter names mapped to their values. Returns self : object Returns self. If callable, it should be a custom evaluation metric, see note below for more details. The model will train until the validation score stops improving. Requires at least one validation data and one metric. If there's more than one, will check all of them.

But the training data is ignored anyway. If True, the eval metric on the eval set is printed at each boosting stage. If 'auto' and data is pandas DataFrame, data columns names are used. If list of int, interpreted as indices. If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used. All values in categorical features should be less than int32 max value Large values could be memory consuming. Consider using consecutive integers starting from zero. All negative values in categorical features will be treated as missing values.

The output cannot be monotonically constrained with respect to a categorical feature.Here comes…. Light GBM into the picture. Many of you might be familiar with the Light Gradient Boosting, but you will have a solid understanding after reading this article.

The most natural question that will come to your mind is — Why another boosting machine algorithm? Well, you guessed it right!!! Light GBM is a fast, distributed, high-performance gradient boosting framework based on decision tree algorithm, used for ranking, classification and many other machine learning tasks. Since it is based on decision tree algorithms, it splits the tree leaf wise with the best fit whereas other boosting algorithms split the tree depth wise or level wise rather than leaf-wise.

So when growing on the same leaf in Light GBM, the leaf-wise algorithm can reduce more loss than the level-wise algorithm and hence results in much better accuracy which can rarely be achieved by any of the existing boosting algorithms. Before is a diagrammatic representation by the makers of the Light GBM to explain the difference clearly.

In simple terms, Histogram-based algorithm splits all the data points for a feature into discrete bins and uses these bins to find the split value of the histogram.

While it is efficient than the pre-sorted algorithm in training speed which enumerates all possible split points on the pre-sorted feature values, it is still behind GOSS in terms of speed. So what makes this GOSS method efficient? As we know instances with small gradients are well trained small training error and those with large gradients are undertrained.

A naive approach to downsample is to discard instances with small gradients by solely focussing on instances with large gradients but this would alter the data distribution. In a nutshell, GOSS retains instances with large gradients while performing random sampling on instances with small gradients.

Having a large number of leaves will improve accuracy, but will also lead to overfitting. The parameter can greatly assist with overfitting: larger sample sizes per leaf will reduce overfitting but may lead to under-fitting. Shallower trees reduce overfitting. The simplest way to account for imbalanced or skewed data is to add weight to the positive class examples:.

In addition to the parameters mentioned above the following parameters can be used to control overfitting:. Both values need to be set for bagging to be used. The frequency controls how often iteration bagging is used. Smaller fractions and frequencies reduce overfitting.

Smaller fractions reduce overfitting. Accuracy may be improved by tuning the following parameters:. The overall parameters have been divided into 3 categories by XGBoost authors:. These define the overall functionality of XGBoost. Select the type of model to run at each iteration.