According to my understanding, RF selects features randomly and hence is hard to overfit. But, in sklearn Gradient boosting also offers the option of max_features which can help to prevent overfitting. So, why would anyone use Random forest?
Can anyone explain when to use Gradient boosting vs Random forest based on the given data?
Any help is highly appreciated.
According to my personal experience, Random Forest could be a better choice when..
You train a model on small data set.
Your data set has few features to learn.
Your data set has low Y flag count or you try to predict a situation that has low chance to occur or rarely occurs.
In these situations, Gradient Boosting algorithms like XGBoost and Light GBM can overfit (though their parameters are tuned) while simple algorithms like Random Forest or even Logistic Regression may perform better. To illustrate, for XGboost and Ligh GBM, ROC AUC from test set may be higher in comparison with Random Forest but shows too high difference with ROC AUC from train set.
Despite the sharp prediction form Gradient Boosting algorithms, in some cases, Random Forest take advantage of model stability from begging methodology (selecting randomly) and outperform XGBoost and Light GBM. However, Gradient Boosting algorithms perform better in general situations.
Similar question asked on Quora:
https://www.quora.com/How-do-random-forests-and-boosted-decision-trees-compare
I agree with the author at the link that random forests are more robust -- they don't require much problem-specific tuning to get good results. Besides that, a couple other items based on my own experience:
Random forests can perform better on small data sets; gradient boosted trees are data hungry
Random forests are easier to explain and understand. This perhaps seems silly but can lead to better adoption of a model if needed to be used by less technical people
I think that's also true. I have also read on this page How Random Forest Works
There explains the advantages of random forest. like this :
For applications in classification problems, Random Forest algorithm
will avoid the overfitting problem
For both classification and
regression task, the same random forest algorithm can be used
The Random Forest algorithm can be used for identifying the most
important features from the training dataset, in other words,
feature engineering.
Related
Can I use AdaBoost with random forest as a base classifier? I searched on the internet and I didn't find anyone who does it.
Like in the following code; I try to run it but it takes a lot of time:
estimators = Pipeline([('vectorizer', CountVectorizer()),
('transformer', TfidfTransformer()),
('classifier', AdaBoostClassifier(learning_rate=1))])
RF=RandomForestClassifier(criterion='entropy',n_estimators=100,max_depth=500,min_samples_split=100,max_leaf_nodes=None,
max_features='log2')
param_grid={
'vectorizer__ngram_range': [(1,2),(1,3)],
'vectorizer__min_df': [5],
'vectorizer__max_df': [0.7],
'vectorizer__max_features': [1500],
'transformer__use_idf': [True , False],
'transformer__norm': ('l1','l2'),
'transformer__smooth_idf': [True , False],
'transformer__sublinear_tf': [True , False],
'classifier__base_estimator':[RF],
'classifier__algorithm': ("SAMME.R","SAMME"),
'classifier__n_estimators':[4,7,11,13,16,19,22,25,28,31,34,43,50]
}
I tried with the GridSearchCV, I added the RF classifier into the AdaBoost parameters.
if I use it would the accuracy increase?
No wonder you have not actually seen anyone doing it - it is an absurd and bad idea.
You are trying to build an ensemble (Adaboost) which in itself consists of ensemble base classifiers (RFs) - essentially an "ensemble-squared"; so, no wonder about the high computation time.
But even if it was practical, there are good theoretical reasons not to do it; quoting from my own answer in Execution time of AdaBoost with SVM base classifier:
Adaboost (and similar ensemble methods) were conceived using decision trees as base classifiers (more specifically, decision stumps, i.e. DTs with a depth of only 1); there is good reason why still today, if you don't specify explicitly the base_classifier argument, it assumes a value of DecisionTreeClassifier(max_depth=1). DTs are suitable for such ensembling because they are essentially unstable classifiers, which is not the case with SVMs, hence the latter are not expected to offer much when used as base classifiers.
On top of this, SVMs are computationally much more expensive than decision trees (let alone decision stumps), which is the reason for the long processing times you have observed.
The argument holds for RFs, too - they are not unstable classifiers, hence there is not any reason to actually expect performance improvements when using them as base classifiers for boosting algorithms, like Adaboost.
Short answer:
It's not impossible.
I don't know if there's anything wrong with doing so in theory, but I tried this once and the accuracy increased.
Long answer:
I tried it on a typical dataset with n rows of p real-valued features, and a label list of length n. In case it matters, they are embeddings of nodes in a graph obtained by the DeepWalk algorithm, and the nodes are categorized into two classes. I trained a few classification models on this data using 5-fold cross validation, and measured common evaluation metrics for them (precision, recall, AUC etc.). The models I have used are SVM, logistic regression, random Forest, 2-layer perceptron and Adaboost with random forest classifiers. The last model, Adaboost with random forest classifiers, yielded the best results (95% AUC compared to multilayer perceptron's 89% and random forest's 88%). Sure, now the runtime has increased by a factor of, let's say, 100, but it's still about 20 mins, so it's not a constraint to me.
Here's what I thought: Firstly, I'm using cross validation, so there's probably no overfitting flying under the radar. Secondly, both are ensemble learning methods, but random forest is a bagging method, wheras Adaboost is a boosting technique. Perhaps they're still different enough for their combination to make sense?
I am trying to solve a regression problem by predicting a continuous value using machine learning. I have a dataset which composed of 6 float columns.
The data come from low price sensors, this explain that very likely we will have values that can be considered out of the ordinary. To fix the problem, and before predicting my continuous target, I will predict data anomalies, and use him as a data filter, but the data that I have is not labeled, that's mean I have unsupervised anomaly detection problem.
The algorithms used for this task are Local Outlier Factor, One Class SVM, Isolation Forest, Elliptic Envelope and DBSCAN.
After fitting those algorithms, it is necessary to evaluate them to choose the best one.
Can anyone have an idea how to evaluate an unsupervised algorithm for anomaly detection ?
The only way is to generate synthetic anomalies which mean to introduce outliers by yourself with the knowledge of how a typical outlier will look like.
I am trying to use machine learning to predict a dataset. It is a regression problem with 180 input features and 1 continuously-valued output. I try to compare deep neural networks, random forest regression, and linear regression.
As I expect, 3-hidden-layer deep neural networks outperform other two approaches with a root mean square error (RMSE) of 0.1. However, I unexpected to see that random forest even performs worse than linear regression (RMSE 0.29 vs. 0.27). In my expectation, the random forest can discover more complex dependencies between features to decrease error. I have tried to tune the parameters of random forest (number of trees, maximum features, max_depth, etc.). I also tried different K-cross validation, but the performance is still less than linear regression.
I searched online, and one answer says linear regression may perform better if features have a smooth, nearly linear dependence on the covariates. I do not fully get the point because if that is the case, should not deep neural networks give much performance gain?
I am struggling to give an explanation. Under what situation, random forest is worse than linear regression, but deep neural networks can perform much better?
If your features explain linear relation to the target variable then a Linear Model usually performs well than a Random Forest Model. It totally depends on the linear relations between your features.
That said, Linear models are not superior or the Random Forest is any inferior one.
Try scaling and transforming the data using MinMaxScaler() from scikit-learn to see if the linear model improves further
Pro Tips
If linear model is working like a charm you need to ask your self Why? and How? And get into the basics of both the models to understand why it worked on your data. These questions will lead you to feature engineer better. And as a matter of fact, Kaggle Grand Masters do use Linear Models in stacking to get that top 1% score by capturing the linear relations in the dataset.
So at the end of the day, linear models could wonders too.
I have never seen such learning curves. Am I right, that huge overfitting occurs? The model is fitting better and better to the training data, while it generalizes worse for the test data.
Usually when there is high variance, like here, more examples should help. In this case, they won't, I suspect. Why is that? Why such example of learning curves can't be found easily in literature/tutorials?
Learning curves. SVM, param1 is C, param2 is gamma
You have to remember that SVM is non parametric model, thus more samples does not have to reduce variance. Reduction in variance can be more or less guaranteed for parametric model (like neural net), but SVM is not one of them - more samples mean not only better training data but also more complex model. Your learning curves are typical example of SVM overfitting, which happens a lot with RBF kernel.
Most classification algorithms are developed to improve the training speed. However, is there any classifier or algorithm focusing on the decision making speed(low computation complexity and simple realizable structure)? I can get enough training dataļ¼and endure the long training time.
There are many methods which classify fast, you could more or less sort models by classification speed in a following way (first ones - the fastest, last- slowest)
Decision Tree (especially with limited depth)
Linear models (linear regression, logistic regression, linear svm, lda, ...) and Naive Bayes
Non-linear models based on explicit data transformation (Nystroem kernel approximation, RVFL, RBFNN, EEM), Kernel methods (such as kernel SVM) and shallow neural networks
Random Forest and other committees
Big Neural Networks (ie. CNN)
KNN with arbitrary distance
Obviously this list is not exhaustive, it just shows some general ideas.
One way of obtaining such model is to build a complex, slow model, then use it as a black box label generator to train a simplier model (but on potentialy infinite training set) - thus getting a fast classifier at the cost of very expensive training. There are many works showing that one can do that for example by training a shallow neural network on outputs of deep nn.
In general classification speed should not be a problem. Some exceptions are algorithms which have a time complexity depending on the number of samples you have for training. One example is k-Nearest-Neighbors which has no training time, but for classification it needs to check all points (if implemented in a naive way). Other examples are all classifiers which work with kernels since they compute the kernel between the current sample and all training samples.
Many classifiers work with a scalar product of the features and a learned coefficient vector. These should be fast enough in almost all cases. Examples are: Logistic regression, linear SVM, perceptrons and many more. See #lejlot's answer for a nice list.
If these are still too slow you might try to reduce the dimension of your feature space first and then try again (this also speeds up training time).
Btw, this question might not be suited for StackOverflow as it is quite broad and recommendation instead of problem oriented. Maybe try https://stats.stackexchange.com/ next time.
I have a decision tree which is represented in the compressed form and which is at least 4 times faster than the actual tree in classifying an unseen instance.