Please cite us if you use the software. Click here to download the full example code or to run this example in your browser via Binder. This example reproduces Figure 1 of Zhu et al 1 and shows how boosting can improve prediction accuracy on a multi-class problem.
How to use Grid Search CV in sklearn, Keras, XGBoost, LightGBM in Python
R 1 algorithms are compared. R uses the probability estimates to update the additive model, while SAMME uses the classifications only. R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations. The error of each algorithm on the test set after each boosting iteration is shown on the left, the classification error on the test set of each tree is shown in the middle, and the boost weight of each tree is shown on the right.
R algorithm and therefore are not shown. Total running time of the script: 0 minutes Gallery generated by Sphinx-Gallery. Toggle Menu. Prev Up Next. Multi-class AdaBoosted Decision Trees. Note Click here to download the full example code or to run this example in your browser via Binder. Zou, S. Rosset, T. R' plt.The XGBoost algorithm is effective for a wide range of regression and classification predictive modeling problems.
It is an efficient implementation of the stochastic gradient boosting algorithm and offers a range of hyperparameters that give fine-grained control over the model training procedure. Although the algorithm performs well in general, even on imbalanced classification datasets, it offers a way to tune the training algorithm to pay more attention to misclassification of the minority class for datasets with a skewed class distribution.
Discover SMOTE, one-class classification, cost-sensitive learning, threshold moving, and much more in my new bookwith 30 step-by-step tutorials and full Python source code.
We will generate 10, examples with an approximate minority to majority class ratio. Once generated, we can summarize the class distribution to confirm that the dataset was created as we expected. Finally, we can create a scatter plot of the examples and color them by class label to help understand the challenge of classifying examples from this dataset.
Tying this together, the complete example of generating the synthetic dataset and plotting the examples is listed below. We can see that the dataset has an approximate class distribution with a little less than 10, examples in the majority class and in the minority class. Next, a scatter plot of the dataset is created showing the large mass of examples for the majority class blue and a small number of examples for the minority class orangewith some modest class overlap.
XGBoost is short for Extreme Gradient Boosting and is an efficient implementation of the stochastic gradient boosting machine learning algorithm. The stochastic gradient boosting algorithm, also called gradient boosting machines or tree boosting, is a powerful machine learning technique that performs well or even best on a wide range of challenging machine learning problems.
Tree boosting has been shown to give state-of-the-art results on many standard classification benchmarks. It is an ensemble of decision trees algorithm where new trees fix errors of those trees that are already part of the model. Trees are added until no further improvements can be made to the model. XGBoost provides a highly efficient implementation of the stochastic gradient boosting algorithm and access to a suite of model hyperparameters designed to provide control over the model training process.
The most important factor behind the success of XGBoost is its scalability in all scenarios. The system runs more than ten times faster than existing popular solutions on a single machine and scales to billions of examples in distributed or memory-limited settings. XGBoost is an effective machine learning model, even on datasets where the class distribution is skewed. Before any modification or tuning is made to the XGBoost algorithm for imbalanced classification, it is important to test the default XGBoost model and establish a baseline in performance.
An instance of the model can be instantiated and used just like any other scikit-learn class for model evaluation. For example:. We will use repeated cross-validation to evaluate the model, with three repeats of fold cross-validation.This notebook was developed in response to a query from a fellow archaeologist, so I am using an archaeological dataset for this analysis.
Unfortunately, I did not have a multiclass dataset ICPS elemental datasets, so I had to simulate and bind a third class to the RBGlass1 dataset of the archdata package.
The code is a bit verbose and inefficient because I wanted it to be more readable, so feel free to smooth it over in real use. If there are any errors or omissions, please let me know as mr. The XGBoost algorithm requires that the class labels Site names start at 0 and increase sequentially to the maximum number of classes. This is a bit of an inconvenience as you need to keep track of what Site name goes with which label.
How to Configure XGBoost for Imbalanced Classification
Also, you need to be very careful when you add or remove a 1 to go from the zero based labels to the 1 based labels. You can pretty much ignore this. The test set will not be used in model fitting in this example since we get a cross-validation error estimate from the training data. The test set is used as a hold-out validation sample to the final model fit to all the training data.
Change the 0. The XGBoost algorithm requires the data to be passed as a matrix. Here I use xgb. DMatrix function to make a dataset of class xgb. DMatrix which is native to XGBoost. The advantage to this over a basic matrix is that I can pass it the variables and the label and identify which column is the label. Therefore I do not need to have separate objects for the train and test labels. Here we set the fitting parameters and then fit a series of XGBoost models to each cv.
The code block starts with assigning the number of classes, a bunch of parameters for the XGBoost fit, and out CV parameters. These two parameters tell the XGBoost algorithm that we want to to probabilistic classification and use a multiclass logloss as our evaluation metric. Here is the documents page on XGB training parameters.
The other parameters of note are nrounds and prediction. The nrounds parameter tells XGBoost how many times to iterate. This is inherently tied to the learning rate eta and will most likely require tuning for your data.XGBoost algorithm has become the ultimate weapon of many data scientist. Building a model using XGBoost is easy. But, improving the model using XGBoost is difficult at least I struggled a lot.
This algorithm uses multiple parameters. To improve the model, parameter tuning is must. It is very difficult to get answers to practical questions like — Which set of parameters you should tune? What is the ideal value of these parameters to obtain optimal output? This article is best suited to people who are new to XGBoost. What should you know? It will help you bolster your understanding of boosting in general and parameter tuning for GBM.
He is helping us guide thousands of data scientists. A big thanks to SRK! Table of Contents. When I explored more about its performance and science behind its high accuracy, I discovered many advantages:. I hope now you understand the sheer power XGBoost algorithm. Note that these are the points which I could muster.
You know a few more? Did I whet your appetite? You can refer to following web-pages for a deeper understanding:. These define the overall functionality of XGBoost. There are 2 more parameters which are set automatically by XGBoost and you need not worry about them.
Lets move on to Booster parameters. These parameters are used to define the optimization objective the metric to be calculated at each step. A good news is that xgboost module in python has an sklearn wrapper called XGBClassifier. It uses sklearn style naming convention. The parameters names which will change are:. Well this exists as a parameter in XGBClassifier. I recommend you to go through the following parts of xgboost guide to better understand the parameters and codes:.
We will take the data set from Data Hackathon 3. I have performed the following steps:. Lets start by importing the required libraries and loading the data:. Note that I have imported 2 forms of XGBoost:. The best part is that you can take this function as it is and use it later for your own models.These days, when people talk about machine learning, they are usually referring to the modern nonlinear methods that tend to win Kaggle competetitions: Random Forests, Gradient Boosted Trees, XGBoost, or the various forms of Neural Networks.
People talk about how these modern methods generally provide lower bias and are able to better optimize an objective function than the more traditional methods like Linear Regression or Logistic Regression for classification.
However, when organizations- specifically organizations in heavily regulated industries like finance, healthcare, and insurance - talk about machine learning, they tend to talk about how they can't implement machine learning in their business because it's too much of a "black box. These organizations make underwriting and pricing decisions based on predictons for annual income, credit default risk, probability of death, disease risk, and many others.
They worry about a series of regulatory requirements forcing them to explain why a particular decision was reached on a single sample, in a clear and defensible manner.
Nobody wants to be the first to test a new regulatory standard and it is far easier to continue business-as-usual, so these organizations like their tidy formulas and interpretable coefficients and they won't give them up without good reason. From the perspective of a data scientist, that good reason is lower model bias leading to better predictions further leading to better customer experiences, a reduction in regulatory issues, and ultimately a stronger competitive advantage and higher profits for the enterprise.
The first mover has much to gain, but also a lot to lose. By applying the techniques discussed here it should become clear there are ways to create value and effectively mitigate the regulatory risks involved. In recent years the "black box" nature of nonparametric machine learnings models has given way to several methods that help us crack open what is happening inside a complex model.
Thanks to ongoing research in the field of ML model explainability, we now have at least five good methods with which we can explore the inner workings of our models. An exhaustive review of all methods is outside the scope of this article, but below is a non-exhaustive set of links for those interested in further research:.
In a well-argued pieceone of the team members behind SHAP explains why this is the ideal choice for explaining ML models and is superior to other methods. SHAP stands for 'Shapley Additive Explanations' and it applies game theory to local explanations to create consistent and locally accurate additive feature attributions. If this doesn't make a lot of sense, don't worry, the graphs below will mostly speak for themselves.
In this post I will demonstrate a simple XGBoost example for a binary and multiclass classification problem, and how to use SHAP to effectively explain what is going on under the hood. I will begin with a binary classifier using the Titanic Survival Dataset.
Our target column is the binary survived and we will use every column except nameticketand cabin. Then, we must deal with missing values in the age and embarked columns so we will impute values. Next, we need to dummy encode the two remaining text columns sex and embarked. Finally, we can drop extra columns, assign our X and y, and train our model.
Because decision tree models are robust to multicollinearity and scaling - and because this is a very simple dataset - we can skip the usual EDA and data normalization procedures and jump to model training and evaluation.
Below we train an XGBoost binary classifier using k-fold cross-validation to tune our hyperparameters to ensure an optimal model fit. Next, we will use those optimal hyperparameters to train our final model but first, because the dataset is so small, we will do a final k-fold cross-validation to get stable error metrics and ensure a good fit. Next, we'll fit the final model and visualize the AUC. We can improve further by determining whether we care more about false positives or false negatives and tuning our prediction threshold accordingly, but this is good enough to stop and show off SHAP.
Above, we see the final model is making decent predictions with minor overfit. We know from historical accounts that there were not enough lifeboats for everyone and two groups were prioritized: first class passengers and women with children.
So, sex and pclass are justifiably important, but this method provides precious little to explain precisely why a prediction was made on a case-by-case basis. Now that we have a trained model, let us make a prediction on a random row of data, and then use SHAP to understand why this was predicted. We see the input data of row from the dataset belonging to a 29 year old male Mr.
This is the question a regulator wants answered if this passenger had survived and complains to the authority that he is very much alive and takes great offense at our inaccurate prediction. In this case, the model correctly predicted his unfortunate end, but even when we are right we still need to understand why.Why not automate it to the extend we can? This is perhaps a trivial task to some, but a very important one — hence it is worth showing how you can run a search over hyperparameters for all the popular packages.
There is a GitHub available with a colab buttonwhere you instantly can run the same code, which I used in this post.
In one line: cross-validation is the process of splitting the same dataset in K-partitions, and for each split, we search the whole grid of hyperparameters to an algorithm, in a brute force manner of trying every combination.
In an iterative manner, we switch up the testing and training dataset in different subsets from the full dataset. Grid Search: From this image of cross-validation, what we do for the grid search is the following; for each iteration, test all the possible combinations of hyperparameters, by fitting and scoring each combination separately.
We need a prepared dataset to be able to run a grid search over all the different parameters we want to try. I'm assuming you have already prepared the dataset, else I will show a short version of preparing it and then get right to running grid search. The sole purpose is to jump right past preparing the dataset and right into running it with GridSearchCV.
But we will have to do just a little preparation, which we will keep to a minimum. For the house prices dataset, we do even less preprocessing.Tuning Model Hyper-Parameters for XGBoost and Kaggle
We really just remove a few columns with missing values, remove the rest of the rows with missing values and one-hot encode the columns. For the last dataset, breast cancer, we don't do any preprocessing except for splitting the training and testing dataset into train and test splits. The next step is to actually run grid search with cross-validation. How does it work? Well, I made this function that is pretty easy to pick up and use. At last, you can set other options, like how many K-partitions you want and which scoring from sklearn.
Firtly, we define the neural network architecture, and since it's for the MNIST dataset that consists of pictures, we define it as some sort of convolutional neural network CNN.
Note that I commented out some of the parameters, because it would take a long time to train, but you can always fiddle around with which parameters you want. Surely we would be able to run with other scoring methods, right? Yes, that was actually the case see the notebook.
This was the best score and best parameters:. Next we define parameters for the boston house price dataset. Here the task is regression, which I chose to use XGBoost for. Interested in running a GridSearchCV that is unbiased? I welcome you to Nested Cross-Validation; where you get the optimal bias-variance trade-off and, by the theory, as unbiased of a score as possible.
I would encourage you to check out this repository over at GitHub. I embedded the examples below, and you can install the package by the a pip command: pip install nested-cv. This is implemented at the bottom of the notebook available here. We can set the default for both those parameters, and indeed that is what I have done. Here the code is, and notice that we just made a simple if-statement for which search class to use:.
Running this for the breast cancer dataset, it produces the below results, which is almost the same as the GridSearchCV result which got a score of 0. Most recommended books referral to Amazon are the following, in order. The first one is particularly good for practicing ML in Python, as it covers much of scikit-learn and TensorFlow. I recommend reading the documentation for each model you are going to use with this GridSearchCV pipeline — it will solve complications you will have migrating to other algorithms.
In particular, here is the documentation from the algorithms I used in this posts:. Join my free mini-course, that step-by-step takes you through Machine Learning in Python. I agree to receive news, information about offers and having my e-mail processed by MailChimp. View privacy-policy for more information. Stay up to date!Please cite us if you use the software. The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid.
Read more in the User Guide. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed. Dictionary with parameters names string as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored.
This enables searching over any sequence of parameter settings. A single string see The scoring parameter: defining model evaluation rules or a callable see Defining your scoring strategy from metric functions to evaluate the predictions on the test set. For evaluating multiple metrics, either give a list of unique strings or a dict with names as keys and callables as values.
NOTE that when using custom scorers, each scorer should return a single value. See Specifying multiple metrics for evaluation for an example. Number of jobs to run in parallel. None means 1 unless in a joblib. See Glossary for more details. Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process.
This parameter can be:. None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs. If True, return the average score across folds, weighted by the number of samples in each test set. In this case, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.
Deprecated since version 0.