Main information about experiment, i.e. name, id, size of test set and accuracy on test set.
{{classifier_name}}
Experiment id
{{experiment_id}}
Experiment name:
{{experiment_name}}
Test set size:
{{test_size}}
Accuracy on test set:
{{test_accuracy}}
Variables Importance
Feature importance refers to a class of techniques for assigning scores to input features to a predictive model
that indicates the relative importance of each feature when making a prediction.
{{VariablesI}}
Partial Dependence
Partial dependence plots (PDP) show the dependence between the target response and a set of input
features of interest, marginalizing over the values of all other input features (the 'complement' features).
Intuitively, we can interpret the partial dependence as the expected target response as a function of the input
features of interest.
Due to the limits of human perception the size of the set of input feature of interest must be small
(usually, one or two) thus the input features of interest are usually chosen among the most important features.
{{PartialD}}
Confusion Matrix
The confusion_matrix function evaluates classification accuracy by computing the confusion matrix with each row
corresponding to the true class (Wikipedia and other references may use different convention for axes).
By definition, entry i, j
in a confusion matrix is the number of observations actually in group i, but predicted to be in group j.
{{ConfusionM}}
Decision Chart
Show precision, recall, F-1 for each class.
The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false
positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is
negative.
The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false
negatives. The recall is intuitively the ability of the classifier to find all the positive samples.
The F-1 score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-1
score reaches its best value at 1 and worst score at 0.
{{DecisionC}}
Calibration Curve
When performing classification you often want not only to predict the class label, but also obtain
a probability of the respective label. This probability gives you some kind of confidence on the prediction.
Some models can give you poor estimates of the class probabilities and some even do not support probability
prediction (e.g., some instances of SGDClassifier). The calibration module allows you to better calibrate the
probabilities of a given model, or to add support for probability prediction.
Well calibrated classifiers are probabilistic classifiers for which the output of the predict_proba method can be
directly interpreted as a confidence level. For instance, a well calibrated (binary) classifier should classify
the samples such that among the samples to which it gave a predict_proba value close to 0.8, approximately 80%
actually belong to the positive class.
{{CalibrationC}}
ROC Curve
A receiver operating characteristic (ROC), or simply ROC curve,
is a graphical plot which illustrates the performance of a binary classifier system as its
discrimination threshold is varied. It is created by plotting the fraction of true positives
out of the positives (TPR = true positive rate) vs. the fraction of false positives out of the
negatives (FPR = false positive rate), at various threshold settings. TPR is also known as sensitivity,
and FPR is one minus the specificity or true negative rate.
{{ROCC}}
Density Chart
This density chart illustrates how the model succeeds in recognising (and separating)
the classes (e.g. 0 and 1 in binary classification). It shows the repartition of the
actual classes in the validation set according to the predicted probability of
being of said class learnt by the model.
The two density functions show the probability density of rows in the validation
set that actually belong to the observed class vs. rows that don't.
A perfect model fully separates the density functions:
- the areas under density plots should not overlap
- the density function of 0 class should be entirely on the left
- the density function of 1 class should be entirely on the right
{{DensityC}}
Detailed Metrics
Information about metrics on train and test sets.
{% for key, value in metrics_dict.items() %}
{{key}}
{{value}}
{% endfor %}
Data Preparation
Information about data preparation steps.
{% for key, value in preparation_dict.items() %}
{{key}}
{{value}}
{% endfor %}
Features
This tab shows all the features used by the model, their data type and the number of non-null values.
{{Features}}
Algorithm
Information about all parameters of trained model.