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  • Ensemble Learning to Improve Machine Learning Results

    Aug 22, 20170183;32;Ensemble learning helps improve machine learning results by combining several models. This approach allows the production of better predictive performance compared to a single model. That is why ensemble methods placed first in many prestigious machine learning competitions, such as the Netflix Competition, KDD 2009, and Kaggle.

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  • machine learning Accuracy of a random classifier Cross

    But, how can I compare the accuracy of my classifier without citing a test set? For example if I said my classifier accuracy is 70% (estimated somehow, e.g. Cross Validation), is it good or bad compared to a random classifier?

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  • Performance Measures for Machine Learning

    Performance Measures for Machine Learning. 2 Performance Measures Accuracy Weighted (Cost Sensitive) Accuracy good prediction 0.7 mediocre prediction 0.6 poor prediction false positives and false negatives Slope of line tangent to curve defines the cost ratio ROC Area represents performance averaged over all

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  • classification quot;Goodquot; classifier destroyed my Precision

    Re accuracy paradox, do you mean that the classifier might just be picking the dominant class? Wouldn't that lead to a random looking ROC curve? The quot;goodquot; classifier in your example has a good Precision and a bad ROC; the quot;goodquot; classifier in my case has the opposite, a bad Precision but a good ROC. $\endgroup$ R Greg Stacey Mar 15 '16 at

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  • Machine Learning Classification Methods and Factor Investing

    Classification is a fundamental building block of machine learning. Most machine learning magic starts with classification understanding spoken speech starts with classifying audio patterns as spoken phonemes and words; self driving cars start with classifying images and objects as

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  • Metrics to Evaluate your Machine Learning Algorithm

    Feb 24, 20180183;32;Metrics to Evaluate your Machine Learning Algorithm. Aditya Mishra Blocked Unblock Follow Following. Feb 24, 2018. Evaluating your machine learning algorithm is an essential part of any project. A confusion matrix is a table that is often used to describe the performance of a classification model (or

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  • Evaluation of binary classifiers

    The evaluation of binary classifiers compares two methods of assigning a binary attribute, one of which is usually a standard method and the other is being investigated. There are many metrics that can be used to measure the performance of a classifier or predictor; different fields have different preferences for specific metrics due to different goals.

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  • Comparing two classifier accuracy results for statistical

    Comparing two classifier accuracy results for statistical significance with t test. Ask Question 15. 9 How to statistically compare the performance of machine learning classifiers? 3. How can I define good in a religion that claims no moral authority?

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  • What are the Best Machine Learning Packages in R? R bloggers

    Jun 06, 20160183;32;What are the Best Machine Learning Packages in R? June 6, 2016 function in rpart.plot package is often referred to as the authentic Swiss army knife for so data scientists can make use of e1071 R package which has specialized functions for implementing Naive Bayes Classifier. Support Vector Machines are there to rescue you when you have

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  • Classification Accuracy is Not Enough More Performance

    Recurrence of Breast CancerClassification AccuracyConfusion MatrixAccuracy ParadoxPrecisionRecallF1 ScoreSummaryThe breast cancer dataset is a standard machine learning dataset. It contains 9 attributes describing 286 women that have suffered and survived breast cancer and whether or not breast cancer recurred within 5 years.It is a binary classification problem. Of the 286 women, 201 did not suffer a recurrence of breast cancer, leaving the remaining 85 that did.I think that False Negatives are probably worse than False Positives for this problem. Do you agree? More detailed screening can clear the FaLive Chat
  • What are good metrics for evaluating classifiers? Quora

    Jul 06, 20100183;32;The problem domain and objective is important to consider when choosing between evaluation metrics. It is also usually a good idea to measure things like % improvement over a baseline model like a random classifier, KNN, or naive bayes. For time series prediction, guessing the next value will be the same as the previous value is often a good start.

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  • Machine Learning (Stanford) Coursera Advice for Machine

    Aug 13, 20170183;32;Machine Learning Week 6 Quiz 2 (Machine Learning System Design) Stanford Coursera. Github repo for the Course Stanford Machine Learning (Coursera) Quiz Needs to be viewed here at the repo (because the questions and some image solutions cant

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    Discover all the products from NETZSCH Grinding amp; Dispersing and see a list of their distributors. Contact the manufacturer directly for a quote. rotor classifier mill / impact / horizontal / for powders CSM 50, knife mill / vertical / for cocoa nibs / fine cutting Tango174; 1000 / Tango174; 2000 / Tango174; 4000.

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    A wide variety of egg classifier machine options are available to you, There are 215 egg classifier machine suppliers, mainly located in Asia. The top supplying country is China (Mainland), which supply 100% of egg classifier machine respectively. Egg classifier machine products are most popular in Mid East, Africa, and North America.

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  • How to decide the best classifier based on the data set

    How to decide the best classifier based on the data set provided? the same features and than use that one with best performance. Bit this requires the knowledge of optimal parameter settings

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  • Which machine learning classifier to choose, in general

    Which machine learning classifier to choose, in general? then the classifier doesn't really matter so much, so you should probably just choose a classifier with good scalability. train a model using k 1 folds and predict its performance using the fold you left out. This you do for each possible combination of folds (first leave 1st fold

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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

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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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