I have been trying to figure out the correlation between the error rate and the number of features in both of these models. I watched some videos, and the creator of the video said that a simple model can be better than a complicated model. So I figured that the more features I had the greater the error rate would be. This did not prove to be true in my work, and when I had less features the error rate went up. I'm not sure if I'm doing this incorrectly, or if the guy in the video made a mistake. Can someone care to explain? I also am curious how features relate to Logistic Regression's error rate as well.
Naive Bayes and Logistic Regression are a "generative-discriminative pair," meaning they have the same model form (a linear classifier), but they estimate parameters in different ways.
For feature x and label y, naive Bayes estimates a joint probability p(x,y) = p(y)*p(x|y) from the training data (that is, builds a model that could "generate" the data), and uses Bayes Rule to predict p(y|x) for new test instances. On the other hand, logistic regression estimates p(y|x) directly from the training data by minimizing an error function (which is more "discrimative").
These differences have implications for error rate:
When there are very few training instances, logistic regression might "overfit," because there isn't enough data to estimate p(y|x) reliably. Naive Bayes might do better because it models the entire joint distribution.
When the feature set is large (and sparse, like word features in text classification) naive Bayes might "double count" features that are correlated with each other, because it assumes that each p(x|y) event is independent, when they are not. Logistic regression can do a better job by naturally "splitting the difference" among these correlated features.
If the features really are (mostly) conditionally independent, both models might actually improve with more and more features, provided there are enough data instances. The problem comes when the training set size is small relative to the number of features. Priors on naive Bayes feature parameters, or regularization methods (like L1/Lasso or L2/Ridge) on logistic regression can help in these cases.
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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
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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.
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.
I'm working with sentiment analysis using NB classifier. I've found some information (blogs, tutorials etc) that training corpus should be balanced:
33.3% Positive;
33.3% Neutral
33.3% Negative
My question is:
Why corspus should be balanced? The Bayes theorem is based on propability of reason/case. So for training purpose isn't it important that in real world for example negative tweets are only 10% not 33.3%?
You are correct, balancing data is important for many discriminative models, but not really for NB.
However, it might be still more beneficial to bias P(y) estimators to get better predictive performance (since due to various simplifications models use, probability assigned to minority class can be heaviy underfitted). For NB it is not about balancing data, but literally modifying the estimated P(y) so that on the validation set accuracy is maximised.
In my opinion the best dataset for training purposes if a sample of the real world data that your classifier will be used with.
This is true for all classifiers (but some of them are indeed not suitable to unbalanced training sets in which cases you don't really have a choice to skew the distribution), but particularly for probabilistic classifiers such as Naive Bayes. So the best sample should reflect the natural class distribution.
Note that this is important not only for the class priors estimates. Naive Bayes will calculate for each feature the likelihood of predicting the class given the feature. If your bayesian classifier is built specifically to classify texts, it will use global document frequency measures (the number of times a given word occurs in the dataset, across all categories). If the number of documents per category in the training set doesn't reflect their natural distribution, the global term frequency of terms usually seen in unfrequent categories will be overestimated, and that of frequent categories underestimated. Thus not only the prior class probability will be incorrect, but also all the P(category=c|term=t) estimates.
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.
Googled a lot to find an answer.Then thought this will be the area where some one will be able to answer my doubt.
In classification algorithm we have model and prediction part.
Normally while testing we have accuracy rate.
Likewise is there any accuracy rate/confidence for model in Navie Bayes algorithm.
Evaluation is (usually) not part of the classifier.
It's something you do seperately to evaluate if you did a good job, or not.
If you classify your test data using naive bayes, you can perform exactly the same kind of evaluation as with other classifiers!