Sage-Tips

Which algorithm is more robust to outliers and noise?

The K-means clustering algorithm is sensitive to outliers, because a mean is easily influenced by extreme values. K-medoids clustering is a variant of K-means that is more robust to noises and outliers.

What is robust to the noisy training data?

To my best knowledge, “robustness to noise” (or “noise robustness”) is a slightly different term, that describe the stability of the algorithm performance after adding some noise to your data (sorry for a bit self-evident definition=))

Is decision tree robust to noise?

Hence, it is important to understand how robust is a learning algorithm to such label noise. Experimentally, Decision trees have been found to be more robust against label noise than SVM and logistic regression.

Which of the following classifiers is robust towards noisy data?

The second most robust method is Bagging, but it is not significantly more robust than AdaBoost and Random Forests. Interestingly, AdaBoost is sensitive to noises. Its performance will deteriorate greatly with the increase of noises as shown in the following experiment.

How decision tree is robust to outliers?

Yes. Because decision trees divide items by lines, so it does not difference how far is a point from lines. Most likely outliers will have a negligible effect because the nodes are determined based on the sample proportions in each split region (and not on their absolute values).

What is noise in decision tree?

Decision tree is one of the most popular tools in data mining and machine learning to extract useful information from stored data. However, data repositories may contain noise, which is a random error in data. The serious effect of noise is that it can confuse the learning algorithm to produce a long and complex model.

What makes an algorithm robust?

For a machine learning algorithm to be considered robust, either the testing error has to be consistent with the training error, or the performance is stable after adding some noise to the dataset.

Is Random Forest robust to noise?

A recent study [10] showed that under symmetric label noise and large sample size at each node, the decision tree and random forest algorithms are robust; while in asymmetric noise, they are not robust in general. Many types of real world label noise can be approximated by this simplified noise model.

Which of the following option is true about K NN algorithm?

4) Which of the following option is true about k-NN algorithm? Solution: CWe can also use k-NN for regression problems. In this case the prediction can be based on the mean or the median of the k-most similar instances.

Which algorithms are robust to outliers?

Yes all tree algorithms are robust to outliers. Tree algorithms split the data points on the basis of same value and so value of outlier won’t affect that much to the split.

Why are tree models robust to outliers?

Trees are robust to outliers for the same reason the median is robust. Each split of a node divides the node into two and, while it doesn’t do it based on the median, it does do it similarly. Gradient Boosted Machines (GBM) have become the most popular approach to machine learning.

Is naive Bayes robust to noise?

This research aims to study this effect on a Naive Bayes classifier and to compare it to a Random Forest classifier. In both cases however, it is more robust than a Random Forest classifiers which is immediately and more significantly affected by noise.

What is k-means clustering algorithm?

It determines the set of items that occurs together in the dataset. The goal of the K-means clustering algorithm is to find groups in the data, with the number of groups represented by the variable K. The algorithm works iteratively to assign each data point to one of the K groups based on the features that are provided.

How is crime analysis done using k-means clustering?

As data mining is the appropriate field to apply on high volume crime dataset and knowledge gained from data mining approaches will be useful and support police force. So In this paper crime analysis is done by performing k-means clustering on crime dataset using rapid miner tool.

What is clustering in machine learning?

● Clustering: Clustering is the task of dividing the population or data points into several groups, such that data points in a group are homogenous to each other than those in different groups. There are numerous clustering algorithms, some of them are – “K-means clustering algorithms”, “mean shift”, “hierarchal clustering”, etc.

What is the goal of clustering in statistics?

The goal is to explore the data and find some sort of patterns or structures. ● Clustering: Clustering is the task of dividing the population or data points into several groups, such that data points in a group are homogenous to each other than those in different groups.