Classification problem another way General task assigning a decision class label to a set of unclassified objects described by a fixed set of attributes (features). Given a set of pre classified examples, discover the classification knowledge representation, to be used either as a classifier to classify new
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In this paper we described a relatively simple method to predict a classifier's performance for a given sample size, through the creation and modelling of a learning curve. As prior research suggests, the learning curves of machine classifiers generally follow the inverse power law [1, 27]. Given the purpose of predicting future performance
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In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. Examples are assigning a given email to the quot;spamquot; or quot;non spamquot; class, and assigning a diagnosis to a given patient based
Jun 11, 20160183;32;Classifier A classifier is a special case of a hypothesis (nowadays, often learned by a machine learning algorithm). A classifier is a hypothesis or discrete valued function that is used to assign (categorical) class labels to particular data points.
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I am new to machine learning and try to use scikit learn(sklearn) to deal with a classification problem. Why is svm not so good as decision tree on the same data? Ask Question 8. 3 $\begingroup$ (choice of kernel and hyper parameter tuning is the key to getting good performance from SVMs, they can only be expected to give good results
The cause of poor performance in machine learning is either overfitting or underfitting the data. In this post, you will discover the concept of generalization in machine learning and the problems of overfitting and underfitting that go along with it. Lets get started. Supervised machine
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This study compared the performance of the SVM and k nn classifiers for the classification of respiratory pathologies from the RALE lung sound database. To do so, the MFCC features of respiratory sounds obtained from the RALE database were extracted.
Mar 25, 20130183;32;In a previous blog post, I spurred some ideas on why it is meaningless to pretend to achieve 100% accuracy on a classification task, and how one has to establish a baseline and a ceiling and tweak a classifier to work the best it can, knowing the boundaries. Recapitulating what I said before, a classification task involves assigning which out of a set of categories or labels should be assigned
Naive Bayes classifier is a straightforward and powerful algorithm for the classification task. Even if we are working on a data set with millions of records with some attributes, it is suggested to try Naive Bayes approach. Naive Bayes classifier gives great results when we use it for textual data
How to measure a classifier's performance when close to 100% of the class labels belong to one class? Are there any well known performance tests for classification models on data dealing with very rare events? If you find it important to have a good performance on predicting the 1's, you could use the F measure instead.
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Discretization is typically used as a pre processing step for machine learning algorithms that handle only discrete data. In addition, discretization also acts as a variable (feature) selection method that can significantly impact the performance of classification algorithms used in the analysis of high dimensional biomedical data.
In recent years, the use of machine learning classifiers is of great value in solving a variety of problems in text classification. Sentiment mining is a kind of text classification in which, messages are classified according to sentiment orientation such as positive or negative. This paper extends the idea of evaluating the performance of
Oct 29, 20150183;32;However, while dealing with enormous amount of text data, models performance and accuracy becomes a challenge. The performance of a text classification model is heavily dependent upon the type of words used in the corpus and type of features created for classification. I used several practices to improve the results of my model.
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Choosing a Machine Learning Classifier. And even if the NB assumption doesnt hold, a NB classifier still often does a great job in practice. A good bet if want something fast and easy that performs pretty well. Its main disadvantage is that it cant learn interactions between features (e.g., it cant learn that although you love
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The problem of predictive modeling is to create models that have good performance making predictions on new unseen data. Therefore it is critically important to use robust techniques to train and evaluate your models on your available training data. The more reliable the estimate of the performance
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Dec 29, 20180183;32;Which machine learning classifier to choose, in general? [closed] For classification, Naive Bayes is a good starter, as it has good performances, is highly scalable and can adapt to almost any kind of classification task. New to machine learning. Recommendation needed 2.