# Explain Individual Predictions

Christoph Molnar

How much has each feature of an instance contributed to the prediction? This can be answered by computing the effects for this instance. An interpretation of instance-specific effects only makes sense in comparison to the distribution of the effect for each feature. We want to explain the prediction of the linear model for the 6-th instance from the bicycle dataset. The instance has the following feature values.

Feature | Value |
---|---|

season | WINTER |

yr | 2011 |

mnth | JAN |

holiday | NO HOLIDAY |

weekday | THU |

workingday | WORKING DAY |

weathersit | GOOD |

temp | 1.604356 |

hum | 51.8261 |

windspeed | 6.000868 |

cnt | 1606 |

days_since_2011 | 5 |

To obtain the feature effects of this instance, we have to multiply its feature values by the corresponding weights from the linear regression model. For the value “WORKING DAY” of feature “workingday”, the effect is, 124.9. For a temperature of 1.6 degrees Celsius, the effect is 177.6. We add these individual effects as crosses to the effect plot, which shows us the distribution of the effects in the data. This allows us to compare the individual effects with the distribution of effects in the data.