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--- train_method [2016/12/23 00:47] – ccubuk
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 The method panel allows you to choose a learning algorithm. 2 well established machine learning algorithms exist; Random Forest and Support Vector Machines.
-Random Forest (RF): Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set. https://en.wikipedia.org/wiki/Random_forest
+ * **Random Forest (RF)**: Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set. https://en.wikipedia.org/wiki/Random_forest
-Support Vector Machines (SVM):
+ * **Support Vector Machines (SVM)**: In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. https://en.wikipedia.org/wiki/Support_vector_machine
- the kind of experiment you want to perform. You can choose between three kinds of experimental design:
-Two group comparison: The comparison is performed between the two groups described in the experimental design file. The experimental design file must include two columns: the first one with the names of the samples, the second one with the class to which each sample belongs.
-Correlation with continuous variable: A correlation is performed between the values of each path along the samples and the continuous variable introduced. The experimental design file must include two columns: the first one with the names of the samples, the second one with the value of the variable for each one of the samples.