Learn Before
Disadvantages of Supervised Learning
- Decision boundary might be overtrained if your training set which doesn't have examples that you want to have in a class
- You need to select lots of good examples from each class while you are training the classifier.
- Classifying big data can be a real challenge. 4.Training for supervised learning needs a lot of computation time.
0
0
Tags
Data Science
Related
Which of the following are use-cases of supervised learning?
Methods of supervised statistical learning
Types of supervised learning problems
Use cases of supervised statistical learning
Which ones are true about Supervised statistical learning?
Which of the following are examples of supervised machine learning? Select all that apply.
Categories of supervised learning algorithms
A Basic Supervised Statistical Learning Workflow
Division of dataset in supervised statistical learning
Feature scaling greatly affects which of the following supervised machine learning methods?
Adavantages of supervised learning
Disadvantages of Supervised Learning
Best practices for Supervised Learning
The Supervised Learning Workshop: A New, Interactive Approach to Understanding Supervised Learning Algorithms
Sequence Models
Purpose of supervised statistical learning
Input Values
Search Ranking
Sequence Learning
Target Values
Independent and Identically Distributed (IID) Assumption