Given a database table containing weather data as follows: Where Outlook. Temperature.
Humidity, and Windy are the input variables (predictors), and Play is the output variable
(response or outcome). Please compute the conditional probability P(Humidity = \'High\'|PLAY
= \'Yes\'). Please keep 3 digits after the decimal point, for example. 0.123. The difference
between the logistic regression model and the neural network model is(are) The neural network
model uses hidden layers. The neural network model uses activation function. Both a. and b.
Neither a. nor b.
Solution
Answer
Formula for the required conditional probability is as given below
P(Humidity = \'High\' | PLAY = \'Yes\') = (Number of Humidity = \'High\') / (Number of PLAY
= \'Yes\')
= 7/9
= 0.778
So, the required conditional probabilty obtained is 0.778, rounded upto 3 decimal places..
Given a database table containing weather data as follows Where Out.pdf
1. Given a database table containing weather data as follows: Where Outlook. Temperature.
Humidity, and Windy are the input variables (predictors), and Play is the output variable
(response or outcome). Please compute the conditional probability P(Humidity = 'High'|PLAY
= 'Yes'). Please keep 3 digits after the decimal point, for example. 0.123. The difference
between the logistic regression model and the neural network model is(are) The neural network
model uses hidden layers. The neural network model uses activation function. Both a. and b.
Neither a. nor b.
Solution
Answer
Formula for the required conditional probability is as given below
P(Humidity = 'High' | PLAY = 'Yes') = (Number of Humidity = 'High') / (Number of PLAY
= 'Yes')
= 7/9
= 0.778
So, the required conditional probabilty obtained is 0.778, rounded upto 3 decimal places.
