Differences

This shows you the differences between two versions of the page.

--- prediction [2021/01/29 23:21] – [Model statistics] krian
+++ prediction [2021/01/30 14:22] (current) – [Model explanation] krian
@@ Line 91: / Line 91: @@
 ==== 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.
-Then a table for test model statistics is showed:
+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 |}}
 === Validation of typical split ===
-Then you will find several plots for train-test split validation.
+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 precision and recall**:
 {{ ::splittrainpr.png?nolink |}}
@@ Line 106: / Line 111: @@
   * **Split Test receiver operating characteristic**:
 {{ ::splittestroc.png?nolink |}}
 === Probability distribution ===
-Here you can find a boxplot for probability distribution in the test split:
+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 a table for test model statistics [Please Lou write here what is the diff between the 2 tables of test model statistics]:
+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 ====
-{{ ::predictionmodelreport.png?nolink |}}
 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 |}}
+===== Test report =====
+When you select //Use existing predictor// you will have a different report for your test prediction study.
+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:
@@ Line 149: / Line 172: @@
       * Note that all curve visualizations have been done using the specialized R package ''PRROC'' [3]
+/*
 === Breast Cancer Molecular Subtype Classification ===
@@ Line 267: / Line 290: @@
 {{ :test_probability_boxplot.png?400 | ROC curve for the test split. }}
+*/
 ===== Bibliography =====