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--- prediction [2021/01/29 22:35] – [Design data panel] krian
+++ prediction [2021/01/30 14:22] (current) – [Model explanation] krian
@@ Line 31: / Line 31: @@
 {{ ::morethantwoclass.png?nolink |}}
-==== Species ====
+==== Species (For training)====
 Here we must choose the species of our experiment.
 You can choose among:
@@ Line 38: / Line 38: @@
   * Rat (Rattus norvegicus).
 {{ ::species.png?nolink |}}
-==== Experimental design ====
+==== Experimental design (For training)====
 This panel includes further parameters necessary to run an analysis.\\
-**Rank and filter circuits**: Check to obtain the circuits that best differentiate your phenotype. This option is only available from //Prediction// tool.
+**Rank and filter circuits**: Check to obtain the circuits that best differentiate your phenotype. This option is only available for the //Build new predictor// type.
 {{ ::filtercircuits.png?nolink |}}
 ==== Pathways ====
@@ Line 89: / Line 89: @@
 {{ ::circuitvaluespredreport.png?nolink |}}
 This matrix file indicates for each "effector circuit" the level of activation calculated using Hipathia method for each sample.
-==== Model training ====
+==== Model evaluation ====
 Here you can visualize the results from the prediction analysis.
-  * **K-fold cross-validation**: The number of equal sized subsamples in which the original sample is randomly partitioned.
+=== K-fold cross-validation ===
+The number of equal-sized subsamples in which the original sample is randomly partitioned.
+Then a table for test model statistics is showed, each value represents the mean across the holdout folds for the corresponding metric or score:
 {{ ::k-foldcrossvalidation.png?nolink |}}
-  * **SVM-RBF hypermeter performance**
-{{ ::hyperparameterpredreport.png?nolink |}}
+=== Validation of typical split ===
-  * **Test model statistics**:
-{{ ::testmodelstatistics.png?nolink |}}
+Then you will find several plots for train-test split validation, where we randomly holdout 30% of the data for the test while the remaining samples are used for training the model. The plots represent the receiver operating characteristic and precision and recall curves for each split.
-  * **Split Train pr**:
+  * **Split Train precision and recall**:
 {{ ::splittrainpr.png?nolink |}}
-  * **Split Train roc**:
+  * **Split Train receiver operating characteristic**:
 {{ ::splittrainroc.png?nolink |}}
-  * **Split Test pr**:
+  * **Split Test precision and recall**:
 {{ ::splittestpr.png?nolink |}}
-  * **Split Test roc**:
+  * **Split Test receiver operating characteristic**:
 {{ ::splittestroc.png?nolink |}}
-==== Prediction model ====
+=== Probability distribution ===
+Here you can find a boxplot for the (predicted) probability distribution of the positive class over the test split with respect to the original labels:
+{{ ::testprobdistboxplot.png?nolink |}}
+Then we show a table with the statistics of the model over the test set, in the same format as the one presented for the k-fold experiment. A well suited model for the problem at hand should not present a huge gap between the performance during the training and testing phases:
+{{ ::testmodelstat.png?nolink |}}
+==== Model explanation ====
+Here you will find a table with the most relevant circuits along with their interaction sign.
+You can download the filtered circuits that best differentiate your phenotype. This section is only available when selecting //Rank and filter circuits// option.
 {{ ::predictionmodelreport.png?nolink |}}
-==== Model statistics ====
+===== Test report =====
-You can download the model statistics.
+When you select //Use existing predictor// you will have a different report for your test prediction study.
-  * **Selected features**: You can download the filtered circuits that best differentiate your phenotype. This section is only available when selecting //Rank and filter circuits// option.
+The test report is divided into four different panels:
+{{ ::testpresdictionreport.png?nolink |}}
+==== Study Information ====
+As explained before, here you can find the information about the current study.
+==== Input Parameters ====
+The parameters with which the test study was launched, such as the name of the used expression file and the Species.
+==== Circuit values ====
+This matrix file indicates for each “effector circuit” the level of activation calculated using Hipathia method for each sample.
+==== Prediction model ====
+This is the most important result, this table is the predicted design file for your selected expression matrix using a previously trained model.
 ===== Workflow =====
 The prediction tool is based on a machine learning module, this module of the Hipathia web tool can be summarized as follows:
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       * Note that all curve visualizations have been done using the specialized R package ''PRROC'' [3]
+/*
 === Breast Cancer Molecular Subtype Classification ===
@@ Line 261: / Line 290: @@
 {{ :test_probability_boxplot.png?400 | ROC curve for the test split. }}
+*/
 ===== Bibliography =====