Isolation forest parameter tuning

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An Optimized Computational Framework for Isolation Forest

For the purpose of this post, I have combined the individual datasets for red and white wine, and assigned both an extra column to distinguish the color of the wine, where 0 represents a red wine and 1 represents a white wine. The purpose of this classification model is to determine whether a wine is red or white. In order to optimize this model to create the most accurate predictions, I will be focusing solely on hyperparameter adjustment and selection. Most generally, a hyperparameter is a parameter of the model that is set prior to the start of the learning process. Different models have different hyperparameters that can be set. For a Random Forest Classifier, there are several different hyperparameters that can be adjusted. In this post, I will be investigating the following four parameters:. A pure leaf is one where all of the data on the leaf comes from the same class. The default value for this parameter is 2, which means that an internal node must have at least two samples before it can be split to have a more specific classification. The default value for this parameter is 1, which means that every leaf must have at least 1 sample that it classifies. More documentation regarding the hyperparameters of a RandomForestClassifier can be found here. Hyperparameters can be adjusted manually when you call the function that creates the model. The different hyperparameters would be tested on the training set, and once the optimized parameter values were chosen, a model would be constructed using the chosen parameters and the testing set, and then would be tested on the training set to see how accurately the model is able to classify the types of wine. When tested on the training set with the default values for the hyperparameters, the values of the testing set were predicted with an accuracy of 0. Validation Curves. A good way to visually check for potentially optimized values of model hyperparameters is with a validation curve. A validation curve can be plotted on a graph to show how well a model performs with different values of a single hyperparameter. In this image, we see that, when testing the values, the best value appears to be In this graph, we see that the highest accuracy value on the cross-validation is close to As we choose higher values for the minimum number of samples required before splitting an internal node, we will have more general leaf nodes, which would have a negative affect on the overall accuracy of our model. It is important to note that, when constructing the validation curves, the other parameters were held at their default values. For the purpose of this post, we will be using all of the optimized values together in a single model. A new Random Forest Classifier was constructed, as follows:. This model resulted in an accuracy of 0. Exhaustive Grid Search. Another way to choose which hyperparameters to adjust is by conducting an exhaustive grid search or randomized search. Randomized searches will not be discussed in this post, but further documentation regarding their implementation can be found here. An exhaustive grid search takes in as many hyperparameters as you would like, and tries every single possible combination of the hyperparameters as well as as many cross-validations as you would like it to perform.

Outlier Detection with Isolation Forest

Isolation Forest or iForest is one of the outstanding outlier detectors proposed in recent years. Yet, in the model setting, it is mainly based on the technique of randomization and, as a result, it is not clear how to select a proper attribute and how to locate an optimized split point on a given attribute while building the isolation tree. Aiming to the two issues, we propose an improved computational framework which allows us to seek the most separable attributes and spot corresponding optimized split points effectively. According to the experimental results, the proposed model is able to achieve overall better performance in the accuracy of outlier detection compared with the original model and its related variants. In the information society, tremendous data are generated to record the events happened in every second. Few of them are special for they are not yielded as our expectations which can be treated as anomalies. Anomalies exist nearly in every field. For example, events like a spam email, an incidental breakdown of the machine, or a hacking behavior on WWW can be regarded as anomalous in real lives. According to the definition given by Hawkins [ 1 ], an anomaly, sometimes called an outlier as well, is far different from the observed normal objects and suspected to be generated from a different mechanism. So, the anomalies should be few and distinct. How to detect them accurately is the main challenge in this field which is commonly called the study of anomaly detection or outlier detection. In reality, the data annotated with normal or abnormal labels are commonly not available which, if existing, are known to be able to provide the prior knowledge to identify them. Meanwhile, the anomalies in the dataset are commonly rare. It suggests that the numbers of the normal instances and the abnormal ones tend to be heavily unbalanced between them. Due to the two reasons, researchers cannot treat the anomaly detection as a typical problem of classification by simply applying the traditional supervised machine learning methodologies. As a result, in the past decades, most proposed models are unsupervised, including statistical models, distance-based models, density-based models, and tree-based models. If a dataset with multivariate attributes contains normal and anomalous points, in contrast to the traditional view angles, we consider that the essential distinction between the anomalies and normal instances lies in the discrepant values on each attribute. In other words, that the different value distributions towards normal and anomalous points on each attribute ensure them separable. Yet, it is not clear how to select the proper split point in the problem settings. Despite the advantages of the iForest, we consider it still has the space to be improved. For example, the selection of the attribute and determination of the split value regarding the chosen attribute are completely arbitrary while applying the model to build the iForest and some more smart methods would be possible to be studied. Since each tree in the iForest, i. Yet, it is not considered in the previous studies. Aiming at the issues mentioned above, we consider proposing a new solution to optimize the tree building process from solving three key questions. Split point refers to a chosen attribute value to partition the data into two sets on an attribute. As there are only two kinds of labels for anomaly detection, we can mark the leaf node with label 1 for normal instance and 0 for the anomaly. For a specified detection process in an iTree, when the step of the decision in the iTree reaches one of the leaf node, it depends on the label of the leaf with 1 or 0 to identify if the instance is normal or not. The layout of the article has an organization as follows. In Section 2related studies in the field of outlier detection will be reviewed. In Section 3we firstly introduce some symbol definitions to be used in the rest of this paper. Then, the motivation of our study and some quantified analysis with regard to the proposed model will be given in Section 4. Some detailed algorithm descriptions are presented in Section 5.

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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. I have a project, in which, one of the stages is to find and label anomalous data points, that are likely to be outliers. As a first step, I am using Isolation Forest algorithm, which, after plotting and examining the normal-abnormal data points, works pretty well. To somehow measure the performance of IF on the dataset, its results will be compared to the domain knowledge rules. Kind of heuristics where we have a set of rules and we recognize the data points conforming to the rules as normal. My task now is to make the Isolation Forest perform as good as possible. However, my data set is unlabelled and the domain knowledge IS NOT to be seen as the 'correct' answer. So I cannot use the domain knowledge as a benchmark. What I know is that the features' values for normal data points should not be spread much, so I came up with the idea to minimize the range of the features among 'normal' data points. I want to calculate the range for each feature for each GridSearchCV iteration and then sum the total range. The minimal range sum will be probably the indicator of the best performance of IF. The problem is that the features take values that vary in a couple of orders of magnitude. Some have range 0,some 0,1 and some as big a 0, or 0,1 To overcome this I thought of 2 solutions:. Is there maybe a better metric that can be used for unlabelled data and unsupervised learning to hypertune the parameters? Does my idea no. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Unsupervised anomaly detection - metric for tuning Isolation Forest parameters Ask Question. Asked 1 year, 8 months ago. Active 1 year, 8 months ago. Viewed 1k times. To overcome this I thought of 2 solutions: Scale all features' ranges to the interval [-1,1] or [0,1]. However, the difference in the order of magnitude seems not to be resolved?

Multivariate Outlier Detection with Isolation Forests

Update: Part 2 describing the Extended Isolation Forest is available here. During a recent project, I was working on a clustering problem with data collected from users of a mobile app. The goal was to classify the users in terms of their behavior, potentially with the use of K-means clustering. However, after inspecting the data it turned out that some users represented abnormal behavior — they were outliers. A lot of machine learning algorithms suffer in terms of their performance when outliers are not taken care of. In order to avoid this kind of problem you could, for example, drop them from your sample, cap the values at some reasonable point based on domain knowledge or transform the data. However, in this article, I would like to focus on identifying them and leave the possible solutions for another time. As in my case, I took a lot of features into consideration, I ideally wanted to have an algorithm that would identify the outliers in a multidimensional space. That is when I came across Isolation Forest, a method which in principle is similar to the well-known and popular Random Forest. In this article, I will focus on the Isolation Forest, without describing in detail the ideas behind decision trees and ensembles, as there is already a plethora of good sources available. The main idea, which is different from other popular outlier detection methods, is that Isolation Forest explicitly identifies anomalies instead of profiling normal data points. Isolation Forest, like any tree ensemble method, is built on the basis of decision trees. In these trees, partitions are created by first randomly selecting a feature and then selecting a random split value between the minimum and maximum value of the selected feature. In principle, outliers are less frequent than regular observations and are different from them in terms of values they lie further away from the regular observations in the feature space. That is why by using such random partitioning they should be identified closer to the root of the tree shorter average path length, i. The idea of identifying a normal vs. A normal point on the left requires more partitions to be identified than an abnormal point right. As with other outlier detection methods, an anomaly score is required for decision making. In the case of Isolation Forest, it is defined as:. More on the anomaly score and its components can be read in [1]. Each observation is given an anomaly score and the following decision can be made on its basis:. For simplicity, I will work on an artificial, 2-dimensional dataset. This way we can monitor the outlier identification process on a plot. First, I need to generate observations. The second group is new observations, coming from the same distribution as the training ones. Lastly, I generate outliers. Figure 2 presents the generated dataset. Now I need to train the Isolation Forest on the training set. I am using the default settings here. Okay, so now we have the predictions. How to assess the performance? We know that the test set contains only observations from the same distribution as the normal observations. So, all of the test set observations should be classified as normal. And vice versa for the outlier set. At first, this looks pretty good, especially considering the default settings, however, there is one issue still to consider. As the outlier data was generated randomly, some of the outliers are actually located within the normal observations. To inspect it more carefully, I will plot the normal observation dataset together with a labeled outlier set. We can see that some of the outliers lying within the normal observation sets were correctly classified as regular observations, with a few of them being misclassified. What we could do is to try different parameter specifications contamination, number of estimators, number of samples to draw for trining the base estimators, etc.

Spotting Outliers With Isolation Forest Using sklearn

Recently, I was struggling with a high-dimensional dataset that had the following structure: I found a very small amount of outliers, all easily identifiable in scatterplots. However, one group of cases happened to be quite isolated, at a large distance from more common cases, on a few variables. Therefore, when I tried to remove outliers that were at three, four, or even five standard deviations from the mean, I would also delete this group. Fortunately, I ran across a multivariate outlier detection method called isolation forest, presented in this paper by Liu et al. This unsupervised machine learning algorithm almost perfectly left in the patterns while picking off outliers, which in this case were all just faulty data points. First, some outlier theory. Multivariate outliers, which we are discussing in this post, are essentially cases that display a unique or divergent pattern on variables. What I find isolation forests to do well, is that they first start at picking off the false or bad cases, and only when those are all identified, will start on the valid, abnormal cases. This is because valid cases, however abnormal, are often still grouped together, where bad cases are truly unique. This is not true for all analyses; if a default is for example, there may be many cases with that value on some variable. As the name suggests, isolation forests are based on random forests. On each iteration, the tree gets to make one split on one of the included variables to removing the most entropy, or degree of uncertainty. For example, a decision tree could first split cases into younger and older people, when predicting SES. Subsequently, it could split the younger group into people with and without college degrees to remove entropy, and so on. Introduce random forests; large, powerful ensembles of trees, in which individual quality of each tree is diminished due to random splits, but with low prediction error due to trees outperforming other trees gaining a larger weighting in the final decision. An isolation forest is based on the following principles according to Liu et al. Therefore, given a decision tree whose sole purpose is to identify a certain data point, less dataset splits should be required for isolating an outlier, than for isolating a common data point. This is illustrated in the following plot:. Based on this, essentially what an isolation forest does, is construct a decision tree for each data point. In each tree, each split is based on selecting a random variable, and a random value on that variable. Subsequently, data points are ranked on how little splits it took to identify them. Isolation forests perform well because they deliberately target outliers, instead of defining abnormal cases based on normal case behaviour in the data. Now for the practical bit. I generate a large sample of definite inliers, then some valid outliers, then some bad cases.

Jan van der Vegt: A walk through the isolation forest - PyData Amsterdam 2019

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