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.
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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
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.
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.
Let's say I have 30,000 vibration sensors monitoring 30,000 drills (1 sensor per drill) in different workplaces. I need to detect anomalies in vibration patterns.
Given we have enough historical data, how would you go about creating models for this problem?
This is a somewhat ambiguous question, however you can follow the following broad steps to perform anomaly detection:
Load the data into your computing environment, maybe Python, MATLAB, or R. This is assuming your data can fit into memory, else you may want to consider setting up an Hadoop or Spark cluster on Amazon EC2 or other virtual clusters.
You should perform some EDA to understand your data better. This will reveal more on the underlying struture of the data, what kind of distribution is it from, etc.
Make rough visual plots of your data if possible. This will come in handy when you need to polish some final plots for a presentation when reporting your analysis.
Based on the EDA, you can then intuitivey prepare your data for processing. You may need to transform, rescale or standardize the dataset before applying any Machine Learning technique for Anomaly detection.
For supervised datasets (i.e. labels are provided), you may consider algorithms such as SVM, Neural Networks, XGBoost or any other appropriate supervised technique. However, great care much be taken in evaluating the results because typical to anomaly detection datasets, there is more often than not a very small number of positive examples (y = 1) with respect to the total number of examples. This is called class imbalance. There are various ways of mitigating this problem. See Class Imbalance Problem.
For unsupervised datasets, techniques such as the density based methods (i.e. Local Outlier Factor (LOF) and its varieties, k-Nearest Neighbor (kNN) -> its a very popular method), One-class SVM, etc. A monograph of unsupervised methods for anomaly detection is detailed in this study. A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data
N.b..
- Don't forget to consider rudimentary ML practices when building your models such as: splitting into training set/ test set or exploring resampling methods such as k-fold CV, LOOCV, etc to control bias/ variance in your results.
- Explore other techniques such as Ensemble methods (i.e. Boosting & Bagging algorithms) to improde model accuracy.
- Deep learning techniques such as the Muli-layer Perceptron can be explored on this problem. If there is some time-series component, a Recurrent Neural Network, RNN can be explored.
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 data，and 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.
I've learned the Logistic Regression for some days, and i think the logistic regression's dataset's labels needs to be 1 or 0, is it right ?
But when i lookup the libSVM library's regression dataset, i see the label values are continues number(e.g. 1.0086,1.0089 ...), did i miss something ?
Note that the libSVM library could be used for regression problem.
Thanks so much !
Contrary to its name, logistic regression is a classification algorithm and it outputs class probability conditioned on the data point. Therefore the training set labels need to be either 0 or 1. For the dataset you mentioned, logistic regression is not a suitable algorithm.
SVM is a classification algorithm and it uses the input labels -1 or 1. It is not a probabilistic algorithm and it doesn't output class probabilities. It also can be adapted to regression.
Are you using a 3rd party library or programming this yourself? Generally the labels are used as ground truth so you can see how effective your approach was.
For example if your algo is trying to predict what a particular instance is it might output -1, the ground truth label will be +1 which means you did not successfully classify that particular instance.
Note that "regression" is a general term. To say someone will perform regression analysis doesn't necessarily tell you what algorithm they will be using, nor all of the nature of the data sets. All it really tells you is that you have a set of samples with features which you want to use to predict a single outcome value (a model for conditional probability).
One major difference between logistic regression and linear regression is that the former is usually trained on categorical, binary-labeled sample sets; while the latter is trained on real-labeled (ℝ) sample sets.
Any time your labels are real valued, it means you're probably going to use linear regression or similar, or else convert those real valued labels to categorical labels (e.g. via thresholds or bins) if you want to in fact use logistic regression. There is potentially a big difference in the quality and interpretation of your results though, if you try to convert from one such problem setup to another.
See also Regression Analysis.