Category Archives: Unsupervised Learning
Limitations of supervised learning
Supervised learning is a machine learning technique in which a model is trained on labeled data, meaning that the data consists of input features and the corresponding correct output labels. The goal of supervised learning is to make predictions on new, unseen data based on the patterns learned from the training data. While supervised learning…
Missing values and supervised learning
Missing values in supervised learning can be a major issue, as they can reduce the accuracy of the model and lead to poor performance. Several approaches can be taken to address missing values in supervised learning, including: Ultimately, the best approach to dealing with missing values in supervised learning will depend on the dataset’s specific…