How to interpret a linear regression effects graph?

could someone tell me how to interpret the following graph?

It corresponds to a graph in which the effects of the variables in a linear regression are observed, but its interpretation is not clear to me.

Why in working day only half a graph is shown? Why doesn't weathersit have whiskers? Why holiday is simply a line at 0?

Here is a brief summary of the variables:

workingday : if day is neither weekend nor holiday is 1, otherwise is 0.

windspeed: Normalized wind speed. The values are divided to 67 (max)

weathersit :

  • 1: Clear, Few clouds, Partly cloudy, Partly cloudy
  • 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
  • 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
  • 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog

temp : Normalized temperature in Celsius. The values are derived via (t-t_min)/(t_max-t_min), t_min=-8, t_max=+39 (only in hourly scale)

season : season (1:winter, 2:spring, 3:summer, 4:fall)

hum: Normalized humidity. The values are divided to 100 (max)

holiday : weather day is holiday or not

Topic interpretation linear-regression regression machine-learning

Category Data Science

1
answered at

Note: you didn't mention what this is for, i.e. the target variable that this model is supposed to predict.

Anyway this graph shows for each independent variable (feature) its effect on predicting the dependent variable (target). A high absolute value (positive or negative) means that the feature actually helps knowing the target to some extent, whereas a value close to zero means that the feature doesn't help at all (or very little). For example "holiday" brings zero information for knowing the target.

For every feature a range of values is shown as a boxplot, it's normal that these can have different shapes. A narrow boxplot like "working day" indicates very little variance (i.e. little uncertainty). The thick line in the middle is usually the median. The whiskers show how far outlier values reach, sometimes there's no outlier.

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