classification techniques in machine learningprovides a comprehensive and comprehensive pathway for students to see progress after the end of each module. With a team of extremely dedicated and quality lecturers,classification techniques in machine learningwill not only be a place to share knowledge but also to help students get inspired to explore and discover many creative ideas from themselves.
The methods used for the classification areLogistic Regression,SVM(Support Vector Machine),CART(Classification andRegression Trees),Naïve Bayes classificationandRandom Forest Algorithm...Read more +
Classification techniquesare used when the variable to be predicted is categorical. A common example ofclassificationproblem is trying to classify an Iris flower among its three different species. Logistic regression is aclassificationtechnique borrowed bymachine learningfrom the field of statistics. Logistic Regression is a statistical ...Read more +
Dec 01, 2017· The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.Techniquesof SupervisedMachine Learning algorithmsincludelinearand logistic regression,multi-class classification, Decision Trees and supportvector machines.Read more +
Aug 19, 2020·ClassificationPredictive Modeling.In machine learning,classificationrefers to a predictive modeling problem where a class label is predicted for a given example of input data. Examples ofclassificationproblems include: Given an example, classify if it is spam or not. Given a handwritten character, classify it as one of the known characters.Read more +
Jul 10, 2020· Decision trees frequently perform well on imbalanced data. In modernmachine learning, tree ensembles (Random Forests, Gradient Boosted Trees, etc.) almost always outperform singular decision trees, so we’ll jump right into those: Tree base algorithm work bylearninga hierarchy of if/else questions. This can force both classes to be addressed.Read more +
Jun 10, 2007· This paper describes various supervisedmachine learning classification techniques. Of course, a single chapter cannot be a complete review of all supervisedmachine learning classificationalgorithms (also known inductionclassificationalgorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the ...Read more +
Therefore, I decided to apply somemachine learningmodels to figure out what makes a good quality wine! For this project, I used Kaggle’s Red Wine Quality dataset to build variousclassificationmodels to predict whether a particular red wine is “good quality” or not. Each wine in this dataset is given a “quality” score between 0 and 10.Read more +
Mar 01, 2006· The proposedclassificationscheme effectively mimics experts'classificationprocedure and automates theclassificationtask. In the case-study of soilclassificationusing data from cone penetration testing, the predictive accuracy of the classifiers on the test set even for the most complex problem was found to be high (83%).Read more +
Machine learningcan play an important role in the music streaming task. This research article proposes amachine learningbased model for theclassificationof music genre.Read more +
Categorizing music files according to their genre is a challenging task in the area of music information retrieval (MIR). In this study, we compare the performance of two classes of models. The first is a deeplearningapproach wherein a CNN model is trained end-to-end, to predict the genre label of an audio signal, solely using its spectrogram. The second approach utilizes hand-crafted ...Read more +
Regression vs.Classification in Machine Learning. Regression andClassificationalgorithms are SupervisedLearningalgorithms. Both the algorithms are used for predictionin Machine learningand work with the labeled datasets. But the difference between both is how they are used for differentmachine learningproblems.Read more +
Aug 07, 2020· Below figure illustrates differences in sentiment polarityclassificationbetween the two approaches: traditionalmachine learning(Support VectorMachine(SVM), Bayesian networks, or decision trees) and deeplearning techniques. Key DeepLearning techniques, which can be …Read more +
Jul 02, 2019· To recap, we have covered some of the the most importantmachine learningalgorithms for data science: 5 supervisedlearning techniques- Linear Regression, Logistic Regression, CART, Naïve Bayes, KNN. 3 unsupervisedlearning techniques- Apriori, K-means, PCA. 2 ensemblingtechniques- Bagging with Random Forests, Boosting with XGBoost.Read more +
Oct 08, 2020·Classification in Machine Learning.Classification in Machine Learning: Supervisedlearning techniquescan be broadly divided into regression andclassificationalgorithms. In this session, we will be focusing onclassification in Machine Learning. We’ll go through the below example to understandclassificationin a better way.Read more +
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