{% extends "global/Page.html" %} {% load staticfiles otree %} {% block title %} Understanding Check - 2/3 {% endblock %} {% block content %}
Statements
  1. If a feature (e.g. "1_year_ago" in this example) "pushes" the output of the ML algorithm to lower values for one forecast, it will be "pushing" the output to lower values for all other forecasts made by the ML algorithm as well.
  2. Generally speaking, if a feature value is lower than the "base value" (<104.7), it always gets assigned a negative bold value and will hence never "push" the output to higher values.
  3. Features with small bold values (e.g. "1_day_ago" in this example) do not impact the the output much for this forecast.
(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.x2 %} {% next_button %} {% endblock %}