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--- worked_example_prediction_train_and_test [2021/01/30 19:27] – [Prediction evaluation] cloucera
+++ worked_example_prediction_train_and_test [2021/01/31 17:33] (current) – [Test report] cloucera
@@ Line 69: / Line 69: @@
 **Model Analysis**
-CV Performance [KINZA aquí no debería de verse la tabla de estadśiticas?]:
+CV Performance:
+{{ ::model_stats.png?nolink |}}
 {{ :model_stats.txt| CV stats }}
@@ Line 176: / Line 177: @@
 This is the most important result of our predictor, which is a matrix with three columns:
-  * Sample name: all the 125 samples in the used expression matrix file.
+  * Sample name: all the 125 samples (104 luminal A, 21 luminal B) in the used expression matrix file.
   * Prediction: the predicted group LumB (Luminal B) or LumA (Luminal A)
   * Probability LumB: this is the probability of being lumB, if it is 1 that means the predictor is 100% sure that the given result will be LumB.
 You can download the matrix of predicted experimental design by clicking on //Prediction results//.
 ===== Prediction evaluation =====
-Note that for this example we know beforehand the ground truth labels so we can compute the classification metrics as in the simulated split during the training phase. The ROC and PR curves are quite similar to those of the simulated split which inform us of the good generalization capabilities of the tool for this problem. The trend can also be observed from the companion metrics table.
+Note that for this example we know beforehand the ground truth labels so we can compute the classification metrics as in the simulated split during the training phase. The ROC and PR curves are quite similar to those of the simulated split which inform us of the good generalization capabilities of the tool for this problem. The trend can also be observed from the companion metrics table and the confusion matrix.
 {{ :split_prediction_roc.png?400 | ROC curve for the holdout split }}
@@ Line 191: / Line 193: @@
 {{ :split_prediction_probability_boxplot.png?400 | Probability distribution of th SVM for the holdout split }}
-[KINZA Convierte a formato tabla lo siguiente. Lo que estaba antes no estaba calculado con la función del modelo.]
+^ statistic	^ value ^
+| Sensitivity	| 0.761904761904762 |
-statistic	value
+| Specificity	| 0.913461538461538 |
+| Positive Predictive Value	| 0.64 |
-Sensitivity	0.761904761904762
+| Negative Predictive Value	| 0.95 |
+| False Positive Rate	| 0.0865384615384616 |
-Specificity	0.913461538461538
+| False Negative Rate	| 0.238095238095238 |
+| Likelihood Ratio Positive	| 8.8042328042328 |
-Positive Predictive Value	0.64
+| Likelihood Ratio Negative	| 0.260651629072682 |
+| Percentage of data points in the main diagonal	| 0.888 |
-Negative Predictive Value	0.95
+| Percentage of data points in the main diagonal corrected for agreement by chance	| 0.627659574468085 |
+| Rand index	| 0.799483870967742|
-False Positive Rate	0.0865384615384616
+| Rand index corrected for agreement by chance	| 0.525491509396793 |
+| Total Accuracy	| 0.888 |
-False Negative Rate	0.238095238095238
-Likelihood Ratio Positive	8.8042328042328
-Likelihood Ratio Negative	0.260651629072682
-Percentage of data points in the main diagonal	0.888
-Percentage of data points in the main diagonal corrected for agreement by chance	0.627659574468085
-Rand index	0.799483870967742
-Rand index corrected for agreement by chance	0.525491509396793
-Total Accuracy	0.888
+^              ^^              Reference              ^^
+^              ^             ^    Lum A    ^    LumB    ^
+^    Prediction    ^    LumA    |    95    |    5    |
+^    :::    ^    LumB    |    9    |    16    |