To<\/span> state<\/span>, one<\/span> can<\/span> not<\/span> conclude<\/span> that<\/span> SVM<\/span> is<\/span> a<\/span> better<\/span> algorithm<\/span> than<\/span> decision<\/span> trees<\/span> or<\/span> linear<\/span> regression<\/span>.<\/p>\n In<\/span> this<\/span> blog<\/span>, we<\/span> are<\/span> going<\/span> to<\/span> look<\/span> into<\/span> the<\/span> top<\/span> machine<\/span> learning<\/span> algorithms<\/span>.<\/p>\n A<\/span> binomial<\/span> logistical<\/span> regression<\/span> is<\/span> restricted<\/span> to<\/span> 2<\/span> binary<\/span> outputs<\/span> and<\/span> more<\/span> than<\/span> 2<\/span> classes<\/span> can<\/span> be<\/span> achieved<\/span> through<\/span> a<\/span> multinomial<\/span> logistic<\/span> regression<\/span>.<\/p>\n Logistic<\/span> regression<\/span> is<\/span> used<\/span> to<\/span> create<\/span> an<\/span> information<\/span> category<\/span> forecast<\/span> for<\/span> weighted<\/span> entry<\/span> scores<\/span> by<\/span> the<\/span> logistic<\/span> sigmoid<\/span> function<\/span>.<\/p>\n Multiple<\/span> regression<\/span> means<\/span> an<\/span> assessment<\/span> of<\/span> regression<\/span> with<\/span> two<\/span> or<\/span> more<\/span> independent<\/span> variables<\/span>.<\/p>\n Logistic<\/span> regression<\/span> is<\/span> traditionally<\/span> a<\/span> two-class<\/span> classification<\/span> problem<\/span> algorithm<\/span>.<\/p>\n K-NN<\/span> is<\/span> used<\/span> in<\/span> a<\/span> range<\/span> of<\/span> machine<\/span> learning<\/span> tasks<\/span>; k-NN<\/span>, for<\/span> example<\/span>, can<\/span> help<\/span> in<\/span> computer<\/span> vision<\/span> in<\/span> hand-written<\/span> letters<\/span> and<\/span> the<\/span> algorithm<\/span> is<\/span> used<\/span> to<\/span> identify<\/span> genes<\/span> that<\/span> are<\/span> contributing<\/span> to<\/span> a<\/span> specific<\/span> characteristic<\/span> of<\/span> the<\/span> gene<\/span> expression<\/span> analysis<\/span>.<\/p>\n SVM.<\/span> The<\/span> support<\/span> vector<\/span> machine(SVM<\/span>) is<\/span> a<\/span> supervised<\/span>, classifying<\/span>, and<\/span> regressing<\/span> machine<\/span> learning<\/span> algorithm<\/span>.<\/p>\n Even<\/span> an<\/span> experienced<\/span> data<\/span> scientist<\/span> cannot<\/span> say<\/span> which<\/span> algorithm<\/span> works<\/span> best<\/span> before<\/span> distinct<\/span> algorithms<\/span> are<\/span> tested<\/span>.<\/p>\n While<\/span> many<\/span> other<\/span> machine<\/span> learning<\/span> algorithms<\/span> exist<\/span>, they<\/span> are<\/span> the<\/span> most<\/span> common<\/span>.<\/p>\n <\/p>\n