{% extends "global/Page.html" %} {% load staticfiles otree %} {% block title %} Understanding Check - 1/3 {% endblock %} {% block content %}
Statements
  1. In this example, starting from the "base value" (104.7), all feature importance are added up and "push" the output value to the position it eventually is to be found (103).
  2. In this example, the three features with the largest impact in "pushing" the output value (no matter if higher or lower) and the feature "5_days_ago" added have no impact on the "output value" as they cancel each other out (add up to 0).
  3. The impact of the features "Avg_total" and "1_month_ago" (-0.6 and 0.5 in this example) will always be the same for other predictions made by the ML algorithm.
(Put your mouse over a feature to see its description)
Avg_total = 104.2
-0.6
Avg_month = 80.2
-2.8
1_day_ago = 104
0.1
4_days_ago = 108
0.3
1_week_ago = 106
1.0
Avg_year = 87.3
1.0
Avg_week = 79.6
-3.7
2_days_ago = 74
-1.3
5_days_ago = 79
0.4
1_month_ago = 100
0.5
Avg_quarter = 82.7
-0.6
Avg_weekday = 116.4
7.3
3_days_ago = 78
-0.1
6_days_ago = 133
0.5
1_year_ago = 90
-4.0
Output = 104.7 (base value) + (-0.6) + (1.0) + (-0.6) + (-2.8) + (-3.7) + (7.3) + (0.1) + (-1.3)
+ (-0.1) + (0.3) + (0.4) + (0.5) + (1.0) + (0.5) + (-4.0) ≈ 103

{% formfield player.x1 %} {% next_button %} {% endblock %}