01. Assessment

Supervised learning requires training labeled data and we know the outcome we are trying to predict. In an unsupervised algorithm your data points are not labeled and we do not necessarily know the outcome we are trying to predict.

Supervised learning requires training unlabeled data and we know the outcome we are trying to predict. In an unsupervised algorithm your data points are labeled and we do not necessarily know the outcome we are trying to predict.

Supervised learning requires training labeled data and we do not know the outcome we are trying to predict. In an unsupervised algorithm your data points are not labeled but we do know the outcome we are trying to predict.

Supervised learning requires training unlabeled data and we do not know the outcome we are trying to predict. In an unsupervised algorithm your data points are labeled but we do know the outcome we are trying to predict.

The ‘k’ refers to the number of neighbors which are used to determine how to generate clusters and we can determine its value by making an educated guess based on the data at hand.

The ‘k’ refers to the number of data points in the training dataset being used to make predictions and we can determine its value by considering as many data points possible.

The ‘k’ refers to the number of clusters and we can determine it’s value using several techniques like the Elbow Method or Silhouette Method.

The ‘k’ refers to the log of the number of clusters and we can determine it’s value using several techniques like the Elbow Method or Silhouette Method.

50%

80%

30%

99%