Evaluate model performance for (binary) classification

Select the outcome, or response, variable of interest. This should be a binary variable, either a factor or an integer with two value (i.e., 0 and 1).

The level in the response variable that is considered a *success*. For example, a purchase or buyer is equal to βyesβ.

Select one or more variables that can be used to *predict* the chosen level in the response variable. This could be a variable, an RFM index, or predicted values from a model (e.g., from a logistic regression estimated using *Model > Logistic regression (GLM)* or a Neural Network estimated using *Model > Neural Network*).

The number of bins to create.

To use the `Profit`

and `ROME`

(Return on Marketing Expenditures) charts, enter the `Margin`

for each sale and the estimated `Cost`

per contact (e.g., mailing costs or opportunity cost of email or text message). For example, if the margin on a sale is $10 (excluding the contact cost) and the contact cost is $1 enter 10 and 1 in the `Margin`

and `Cost`

input windows.

If a `filter`

is active (e.g., set in the *Data > View* tab) generate results for `All`

data, `Training`

data, `Test`

data, or `Both`

training and test data. If no filter is active calculations are applied to all data.

Generate Lift, Gains, Profit, and/or ROME charts.

Add code to *Report > Rmd* to (re)create the analysis by clicking the icon on the bottom left of your screen or by pressing `ALT-enter`

on your keyboard.

If we create a set of four charts in the *Plots* tab we can add a title above the group of plots and impose a two-column layout using `patchwork`

as follows:

```
plot(result, plots = c("lift", "gains", "profit", "rome"), custom = TRUE) %>%
wrap_plots(plot_list, ncol = 2) + plot_annotation(title = "Model evaluation")
```

The single plot can be customized using `ggplot2`

commands (see example below)). See *Data > Visualize* for details.

Predicted probabilities probabilities selected through `Predictor`

are first converted to a class (e.g., a positive or negative outcome) using the values entered in `Margin`

and `Cost`

. It will be profitable to contact a customer if the predicted probability of response exceeds `Cost / Margin`

. For example, if the break-even response rate is 0.1 and the predicted probability of response is 0.25 that customer will be assigned the label *Positive*. If, on the other hand, the predicted probability does not exceed the break-even response rate a customer will be assigned a *Negative* label.

Once each prediction has been converted to a class label (i.e., Positive or Negative) the result is compared to the values of the response variable. The following key measures are shown in the generated table for each predictor.

Label | Description |
---|---|

TP (True Positive) | Number of cases where the positive prediction matches the positive outcome in the data |

FP (False Positive) | Number of cases with a positive prediction but a negative outcome in the data |

TN (True Negative) | Number of cases where the negative prediction matches the negative outcome in the data |

FN (False Negative) | Number of cases with a negative prediction but a positive outcome in the data |

total | Total number of cases (i.e., TP + FP + TN + FN) |

TPR (True Positive Rate) | Proportion of positive outcomes in the data that received a positive prediction (i.e., TP / (TP + FN)). Also known as sensitivity or recall |

TNR (True Negative Rate) | Proportion of negative outcomes in the data that received a negative prediction (i.e., TN / (TN + FP)). Also known as specificity |

precision | Proportion of positive predictions with a positive outcome in the data (i.e., TP / (TP + FP)) |

F-score | The harmonic mean of precision and TPR (sensitivity) |

accuracy | Proportion of all outcomes that was correctly predicted as either positive or negative (i.e., (TP + TN) / total) |

kappa | Corrects the accuracy measure for the probability of generating a correct prediction purely by chance |

profit | Total profitability achieved by targeting all customers with a predicted probability above the break-even response rate |

index | Index of relative profitability achieved across the selected `Predictor` variables (maximum is 1) |

ROME | Return on Marketing Expenditures (ROME) achieved by targeting all customers with a predicted probability above the break-even response rate |

contact | Proportion of customers to contact, i.e., (TP + FP) / total |

AUC | Area Under the ROC Curve (AUC). ROC stands for Receiver Operating Characteristic. |

Add code to *Report > Rmd* to (re)create the analysis by clicking the icon on the bottom left of your screen or by pressing `ALT-enter`

on your keyboard.

Only `kappa`

, `index`

, `ROME`

, and `AUC`

are plotted by default. It is possible to customize the plotted results through *Report > Rmd*. To change the plot use, for example:

The plot can be further customized using `ggplot2`

commands (see example below)). See *Data > Visualize* for details.

To download a table as a csv-files click the download button on the top-right of your screen. To download plots as png files click the download icon on the middle-right of your screen.

The Lift and Gains charts below show little evidence of overfitting and suggest that targeting approximately 65% of customers would maximize profits.

The prediction used in the screen shots above was derived from a logistic regression on the `dvd`

data. The data is available through the *Data > Manage* tab (i.e., choose `Examples`

from the `Load data of type`

drop-down and press `Load`

). The model was estimated using *Model > Logistic regression (GLM)*. The predictions shown below were generated in the *Predict* tab.

For an overview of related R-functions used by Radiant to evaluate (binary) classification models see *Model > Evaluate classification*