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It also compares bagging vs boosting and K-means vs KNN, and defines evaluation and modeling terms like variance inflation factor, precision, recall, and L1/L2 regularization.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/exam/","Exam",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/100-machine-learning-interview-questions-and-answers-391806/391806/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/100-machine-learning-interview-questions-and-answers-391806/391806.png","ImageObject",300,407,{"name":42,"@type":43},"Stanford","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-09","2026-09-25",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",12,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the relationship between machine learning, artificial intelligence, and deep learning?","Question",{"text":63,"@type":64},"Machine learning is a subset of artificial intelligence. 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In bagging, models receive equal weight; in boosting, models are weighted based on performance.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},391806,1790462942,{"code":4,"msg":82,"data":83},"success",[84,88,92,95,100,105,110,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":93,"slug":94},70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":96,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":33,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":140,"language":141,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":12,"update_tm":145,"read_time":98,"preview_page_count":4},2336477552062,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","100 Machine Learning Interview Questions and Answers  \n1. Please Explain Machine Learning, Artificial Intelligence, And Deep Learning?  \nMachine learning is defined as a subset of Artificial Intelligence, and it contains the techniques which enable computers to sort things out from the data and deliver Artificial Intelligence applications. Artificial Intelligence (AI) is a branch of computer science that is mainly focused on building smart machines that can perform certain tasks that mainly require human intelligence. It is the venture to replicate or simulate human intelligence in machines.  \nDeep learning can be defined as a class of machine learning algorithms in Artificial Intelligence that mainly uses multiple layers to cumulatively extract higher-level features from the given raw input.  \n2. How Difficult Is Machine Learning?  \nMachine Learning is huge and comprises a lot of things. Therefore, it will take more than six months to learn Machine Learning if you spend at least 6-7 hours per day. If you have good hands-on mathematical and analytical skills, then six months will be sufficient for you.  \n3. Can You Explain Kernel Trick In An SVM Algorithm?  \nA Kernel Trick is a method where the Non-Linear data is projected onto a bigger dimension space in order to make it easy to classify the data where it can be linearly divided by a plane.  \n4. Can You List Some Of The Popular Cross-Validation Techniques?  \n1. Holdout Method: This kind of technique works by removing the part of the training data set and sending the same to the model that was trained on the remaining data set to get the required predictions.  \n2. K-Fold Cross-Validation: Here, the data is divided into k subsets so that every time, one among the k subsets can be used as a validation set, and the other k-1 subsets are used as the training set  \n3. Stratified K-Fold Cross-Validation: It works on imbalanced data.  \n4. Leave-P-Out Cross-Validation: Here, we leave p data points out of the training data out of the n data points, then we use the n-p samples to train the model and p points for the validation set.  \n5. Differences Between The Bagging And Boosting Algorithms?  \n\n| Bagging | Boosting |\n| --- | --- |\n| It is a method that merges the same type of predictions. | It is a method that merges the different types of predictions. |\n| It decreases the variance, not the bias | It decreases the bias, not the variance. |\n| Each and every model receives equal weight | Models are weighed based on performance. |\n\n6. What Are Kernels In SVM? Can You List Some Popular Kernels Used In SVM?  \nThe kernel is basically used to set mathematical functions that are used in the Support Vector Machine by providing the window to manipulate the data. Kernel Function is used to transform the training set of data so that a non-linear decision surface will be transformed to a linear equation in a bigger number of dimension spaces.  \nSome of the popular kernels used in SVM are:  \n1. Polynomial kernel  \n2. Gaussian kernel  \n3. Gaussian radial basis function ( RBF)  \n4. Laplace RBF kernel  \n5. Hyperbolic tangent kernel  \n6. Sigmoid kernel  \n7. Bessel function of the first kind Kernel  \n8. ANOVA radial basis kernel  \n7. Can You Explain The OOB Error?  \nAn out-of-bag error called OBB error, also known as an out-of-bag estimate, is a technique to measure the prediction error of random forests, boosted decision trees. Bagging mainly uses subsampling with replacement to create the training samples for the model to learn from them.  \n8. Can You Differentiate Between K-Means And KNN Algorithms?  \n\n| K-Means | KNN algorithms |\n| --- | --- |\n| It is unsupervised machine learning. | It is supervised machine learning. |\n| It is a clustering machine learning algorithm. | It is a classification or regression machine learning algorithm. |\n| Its performance is slow. | It performs much better. |\n| It is an eager learner. | It is a lazy learner. |\n\n9. Explain The Term Variance Inflation Factor Mean?  \nVari","cbCaigih3ry9QMZF","https://ap.wps.com/l/cbCaigih3ry9QMZF","pdf",144738,24,"English","# Interview Questions\n## Core Concepts\n## SVM and Kernels\n## Validation and Ensemble Methods\n## Clustering and Distance-Based Methods\n## Regression, Regularization, and Metrics","[{\"question\":\"What is the relationship between machine learning, artificial intelligence, and deep learning?\",\"answer\":\"Machine learning is a subset of artificial intelligence. Deep learning is a class of machine learning algorithms that uses multiple layers to extract higher-level features from raw input.\"},{\"question\":\"How does the kernel trick work in an SVM?\",\"answer\":\"The kernel trick projects non-linear data into a higher-dimensional space so that classification becomes easier, enabling a linear decision boundary in that transformed space.\"},{\"question\":\"What are the differences between bagging and boosting algorithms?\",\"answer\":\"Bagging merges the same type of predictions and decreases variance, not bias, while boosting merges different types of predictions and decreases bias, not variance. In bagging, models receive equal weight; in boosting, models are weighted based on performance.\"}]","100 Machine Learning Interview Questions and Answers | PDF",1790358850]