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  1. PO
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    <p>Since your outcome variable is numeric, it uses the regression formulation of SVM. I think you want the classification formulation. You can change this by either coercing your outcome into a factor, or setting <code>type="C-classification"</code>.</p> <p><strong>Regression:</strong></p> <pre><code>&gt; model &lt;- svm(vs ~ hp+mpg+gear,data=mtcars) &gt; predict(model) Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive 0.8529506670 0.8529506670 0.9558654451 0.8423224174 Hornet Sportabout Valiant Duster 360 Merc 240D 0.0747730699 0.6952501964 0.0123405904 0.9966162477 Merc 230 Merc 280 Merc 280C Merc 450SE 0.9494836511 0.7297563543 0.6909235343 -0.0327165348 Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental -0.0092851098 -0.0504982402 0.0319974842 0.0504292348 Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla -0.0504750284 0.9769206963 0.9724676874 0.9494910097 Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 0.9496260289 0.1349744908 0.1251344111 0.0395243313 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa 0.0983094417 1.0041732099 0.4348209129 0.6349628695 Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E 0.0009258333 0.0607896408 0.0507385269 0.8664157985 </code></pre> <p><strong>Classification:</strong></p> <pre><code>&gt; model &lt;- svm(as.factor(vs) ~ hp+mpg+gear,data=mtcars) &gt; predict(model) Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive 1 1 1 1 Hornet Sportabout Valiant Duster 360 Merc 240D 0 1 0 1 Merc 230 Merc 280 Merc 280C Merc 450SE 1 1 1 0 Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental 0 0 0 0 Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla 0 1 1 1 Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 1 0 0 0 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa 0 1 0 1 Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E 0 0 0 1 Levels: 0 1 </code></pre> <p>Also, if you want probabilities as your prediction rather than just the raw classification, you can do that by fitting with the probability option.</p> <p><strong>With Probabilities:</strong></p> <pre><code>&gt; model &lt;- svm(as.factor(vs) ~ hp+mpg+gear,data=mtcars,probability=TRUE) &gt; predict(model,mtcars,probability=TRUE) Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive 1 1 1 1 Hornet Sportabout Valiant Duster 360 Merc 240D 0 1 0 1 Merc 230 Merc 280 Merc 280C Merc 450SE 1 1 1 0 Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental 0 0 0 0 Chrysler Imperial Fiat 128 Honda Civic Toyota Corolla 0 1 1 1 Toyota Corona Dodge Challenger AMC Javelin Camaro Z28 1 0 0 0 Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa 0 1 0 1 Ford Pantera L Ferrari Dino Maserati Bora Volvo 142E 0 0 0 1 attr(,"probabilities") 0 1 Mazda RX4 0.2393753 0.76062473 Mazda RX4 Wag 0.2393753 0.76062473 Datsun 710 0.1750089 0.82499108 Hornet 4 Drive 0.2370382 0.76296179 Hornet Sportabout 0.8519490 0.14805103 Valiant 0.3696019 0.63039810 Duster 360 0.9236825 0.07631748 Merc 240D 0.1564898 0.84351021 Merc 230 0.1780135 0.82198650 Merc 280 0.3402143 0.65978567 Merc 280C 0.3829336 0.61706640 Merc 450SE 0.9110862 0.08891378 Merc 450SL 0.8979497 0.10205025 Merc 450SLC 0.9223868 0.07761324 Cadillac Fleetwood 0.9187301 0.08126994 Lincoln Continental 0.9153549 0.08464509 Chrysler Imperial 0.9358186 0.06418140 Fiat 128 0.1627969 0.83720313 Honda Civic 0.1649799 0.83502008 Toyota Corolla 0.1781531 0.82184689 Toyota Corona 0.1780519 0.82194807 Dodge Challenger 0.8427087 0.15729129 AMC Javelin 0.8496198 0.15038021 Camaro Z28 0.9190294 0.08097056 Pontiac Firebird 0.8361349 0.16386511 Fiat X1-9 0.1490934 0.85090660 Porsche 914-2 0.5797194 0.42028060 Lotus Europa 0.4169587 0.58304133 Ford Pantera L 0.8731716 0.12682843 Ferrari Dino 0.8392372 0.16076281 Maserati Bora 0.8519422 0.14805785 Volvo 142E 0.2289231 0.77107694 </code></pre>
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    1. COVery thorough explanation ! Thanks Ian.
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