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What is the best algorithm for a classification task?

What is the best algorithm for a classification task? (relative) performance of algorithms on new problems. as per my knowledge support vector machines are good as for as classification is

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Model evaluation, model selection, and algorithm selection

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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Performance Evaluation of Machine Learning Classifiers in

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

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Performance Metrics for Classification problems in Machine

Nov 11, 20170183;32;Different performance metrics are used to evaluate different Machine Learning Algorithms. For now, we will be focusing on the ones used for Classification problems.

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How to measure a classifier's performance when close to

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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How to measure a classifier's performance when close to

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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high efficient spiral classifier for sale. high quality spiral classifier for mining in quartzry. high capacity wear resistant gold separator Torch Academy. air classifier price high efficient spiral classifier for saleair classifier price wholesale various high quality air classifier price . good performance spiral classifier /mineral separator machine china good performance . energy saving

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Machine learning

A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.

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machine learning Why is svm not so good as decision tree

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 Asked 6 years, (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 when

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How the Naive Bayes Classifier works in Machine Learning

Learn how the naive Bayes classifier algorithm works in machine learning by understanding the Bayes theorem with real life examples. (Opens in new window) Click to share on Facebook (Opens in new window) Amazon go Big data Bigdata Classification classification algorithms clustering algorithms datamining Data mining Datascience data

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Choosing a Machine Learning Classifier blog.echen.me

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Why accuracy alone is a bad measure for classification

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

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new design mining machine flotation cell with good performance

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Predicting sample size required for classification performance

Feb 15, 20120183;32;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, our

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Classification Performance Metrics NLP FOR HACKERS

Classification Performance Metrics. Throughout this blog, we seek to obtain good performance on our classification tasks. Classification is one of the most popular tasks in Machine Learning. Be sure you understand what classification is before going through this tutorial.

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China Good Performance Spiral Classifier Used With Ball

China Good Performance Spiral Classifier Used With Ball Mill , Find Complete Details about China Good Performance Spiral Classifier Used With Ball Mill,Air Classifier Ball Mill,Air Classifier Mill,Screens Classifier from Mineral Separator Supplier or Manufacturer Zhengzhou Yufeng Heavy Machinery Co.,

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Understanding Support Vector Machine algorithm from

Sep 13, 20170183;32;Support vector machine, a machine learning algorithm and its uses in classification and regression. Its a supervised learning algorithm in data science. Tuning parameters value for machine learning algorithms effectively improves the model performance. Lets look at the list of parameters available with SVM.

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What is generalization in machine learning? Quora

In machine learning, generalization usually refers to the ability of an algorithm to be effective across a range of inputs and applications. As an example, say I were to show you an image of dog and ask you to classify that image for me; assumin

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mineral spiral classifier alibaba

good performance new mining classifier machine offers 1,235 mineral spiral classifier products. About 76% of these are mineral separator, 7% are sand washer, and 1% are vibrating screen. A wide variety of mineral spiral classifier options are available to you, such as sprial separator, flotation separator.

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Overfitting Data Mining with Weka

011 Skip to 0 minutes and 11 seconds Hi Before we go on to talk about some more simple classifier methods, we need to talk about overfitting. Any machine learning method may 'overfit' the training data, that's when it produces a classifier that fits the training data too tightly and doesn't generalize well to independent test data.

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new type mineral processing spiral classifier iso

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machine learning Why is svm not so good as decision tree

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 Asked 6 years, (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 when

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Model Selection Francis Tseng

Model Selection. Model selection is the process of choosing between different machine learning approaches e.g. SVM, logistic regression, etc or choosing between different hyperparameters or sets of features for the same machine learning approach e.g. deciding between the polynomial degrees/complexities for linear regression.

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What is the best algorithm for a classification task?

What is the best algorithm for a classification task? (relative) performance of algorithms on new problems. as per my knowledge support vector machines are good as for as classification is

Get Price

Data Mining Evaluation of Classifiers

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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Ore Dressing Supplier Screening Machine Classifier

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Data Mining Evaluation of Classifiers

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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Top 10 Machine Learning Algorithms DeZyre

Jan 29, 20160183;32;Support Vector Machine is a supervised machine learning algorithm for classification or regression problems where the dataset teaches SVM about the classes so that SVM can classify any new data. It works by classifying the data into different classes by finding a line (hyperplane) which separates the training data set into classes.

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Classification Accuracy is Not Enough More Performance

We can see that classification accuracy alone is not sufficient to select a model for this problem. Confusion Matrix. A clean and unambiguous way to present the prediction results of a classifier is to use a confusion matrix (also called a contingency table). For a binary classification problem the table has 2 rows and 2 columns.

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