1. We are going to consider the example from Tom Mitchell book to understand Version Spaces. Consider the example task of learning the target concept "days on which my friend Aldo enjoys his favorite water sport. Table below describes a set of example days, each represented by a set of attributes. The attribute EnjoySport indicates whether or not Aldo enjoys his favorite water sport on this day and is the class label that we will try to predict The task is to learn to predict the value of EnjoySport for an arbitrary day based on the values of its other attributes. In other words, we are learning the concept of (EnjoySport-Yes) Let each input example be a vector of following six attributes . Sky (with possible values Sunny and Rainy) AirTemp (with values Warm and Cold) Humidity (with values Normal and High) . Wind (with values Strong and Weak) . Water (with values Warm and Cool) Forecast (with values Same and Change) Using the Find-S algorithm discussed in the class, find the correct answer below. The training data is shown below: Example Sky AirTemp Humidity Wind Water Forecast EnjoySport Sunny Warm High Strong Cool Change Yes Sunny Warm Normal Strong Warm Same Yes Rainy Cold High Strong Warm Same No Sunny Warm High Strong Warm Same Yes O a) If the fifth training example is Rainy, Warm, High, Strong, Cool, Change), No The new hypothesis will be {Sunny, Warm, ?, Strong, ?,?) b) If the fifth training example is Rainy, Warm, High, Strong, Cool, Change), No The new hypothesis will be ?, ? Strong, ?,?) O c) If the fifth training example is Rainy, Warm, High, Strong, Cool, Change), No The new hypothesis will be {?, Warm, ?, Strong, ?,?) examples, the learnt hypothesis will classify the following example as d) After the algorithm has finished training with the four training data Yes {Sunny, Warm, Normal, Weak, Cool, Change) 1. We are going to consider the example from Tom Mitchell book to understand Version Spaces. Consider the example task of learning the target concept "days on which my friend Aldo enjoys his favorite water sport. Table below describes a set of example days, each represented by a set of attributes. The attribute EnjoySport indicates whether or not Aldo enjoys his favorite water sport on this day and is the class label that we will try to predict The task is to learn to predict the value of EnjoySport for an arbitrary day based on the values of its other attributes. In other words, we are learning the concept of (EnjoySport-Yes) Let each input example be a vector of following six attributes . Sky (with possible values Sunny and Rainy) AirTemp (with values Warm and Cold) Humidity (with values Normal and High) . Wind (with values Strong and Weak) . Water (with values Warm and Cool) Forecast (with values Same and Change) Using the Find-S algorithm discussed in the class, find the correct answer below. The training data is shown below: Example Sky AirTemp Humidity Wind Water Forecast EnjoySport Sunny Warm High Strong Cool Change Yes Sunny Warm Normal Strong Warm Same Yes Rainy Cold High Strong Warm Same No Sunny Warm High Strong Warm Same Yes O a) If the fifth training example is Rainy, Warm, High, Strong, Cool, Change), No The new hypothesis will be {Sunny, Warm, ?, Strong, ?,?) b) If the fifth training example is Rainy, Warm, High, Strong, Cool, Change), No The new hypothesis will be ?, ? Strong, ?,?) O c) If the fifth training example is Rainy, Warm, High, Strong, Cool, Change), No The new hypothesis will be {?, Warm, ?, Strong, ?,?) examples, the learnt hypothesis will classify the following example as d) After the algorithm has finished training with the four training data Yes {Sunny, Warm, Normal, Weak, Cool, Change)
