### Global performance indicators and target statistics

#### Global performance indicators:

- Predictive power measures the accuracy of the predictive model. It takes a value between 0% and 100%. This value should be as close as possible to 100%, without being equal to 100%.
- Predictive confidence indicates the capacity of your predictive model to achieve the same degree of accuracy when you apply it to a new dataset, which has the same characteristics as the training dataset. Prediction confidence takes a value between 0% and 100%. This value should be as close as possible to 100%.

#### Target statistics:

Gives the frequency (%) of the two target categories. In this case 1 and 0 in the training and validation sub-sets.

### Influencer contributions and % detected target

#### Influencer contributions:

Shows the relative importance of each variable used in the predictive model. It examines the influence on the target of the top 5 variables used in the predictive model. It is a useful report to spot if there are leaker variables in a model, as the leaker will have a suspiciously high contribution, overwhelming all of the other variables.

#### % detected target:

Compares the classification model performance (on the validation sub-sample of the data) to a random model and a hypothetically perfect (100% accurate) model.

The % detected target curve compares the model to the hypothetically perfect and random models. It shows the percentage of the total population (x-axis) that corresponds to the % of positive detected targets (y-axis) given by the classification model.

- If the model was perfectly accurate, then the blue model curve would overlap the green perfect model curve. Predictive power = 100%.
- If the model was perfectly inaccurate, then the blue model curve would overlap the red random curve. Predictive power = 0%.