Title: Algorithm summary table
<a name="Algorithmsummarytable-Classification"></a>
### Classification

<table>
<tr><th> algorithm name </th><th> description </th><th> production-ready? </th><th> needs Hadoop? </th><th>
input format </th><th> run command </th></tr>
<tr><td> SGD LogisticRegression command line </td><td> train a logistic regression model
with Stochastic Gradient Descent </td><td> x </td><td> </td><td> CSV with Header </td><td>
trainAdaptiveLogistic </td></tr>
<tr><td> SGD LogisticRegression API </td><td> train a logistic regression model with
Stochastic Gradient Descent </td><td> x </td><td> </td><td> Mahout Vector, feature hashing API </td><td>
custom code </td></tr>
<tr><td> Random Forest </td><td> build a random forest </td><td>  </td><td> x </td><td> CSV without header and
quotes </td><td> BuildForest </td></tr>
</table>

<a name="Algorithmsummarytable-CollaborativeFiltering"></a>
### Collaborative Filtering

<table>
<tr><th> algorithm name </th><th> description </th><th> production-ready? </th><th> needs Hadoop? </th><th>
input format </th><th> run command </th></tr>
<tr><td> Itembased Collaborative Filtering </td><td>compute pairwise item-similarities </td><td> x
</td><td> x </td><td> tab-separated text files </td><td> itemsimilarity </td></tr>
<tr><td> Itembased Collaborative Filtering </td><td> compute recommendations as batch </td><td> x
</td><td> x </td><td> tab-separated text files </td><td> recommenditembased  </td></tr>
<tr><td> Matrix factorization with Alternating Least Squares </td><td> decompose a rating
matrix </td><td>  </td><td> x </td><td> tab-separated text files </td><td> parallelALS	</td></tr>
<tr><td> Matrix factorization </td><td> predict unknown preferences using decomposed
rating matrix </td><td> x </td><td> x </td><td> tab-separated text files </td><td> predictFromFactorization
 </td></tr>