- 1 What is a generalized algorithm?
- 2 What are the three types of machine learning algorithms?
- 3 What type of algorithm is machine learning?
- 4 What are the components of generalization error?
- 5 What is pseudo code in data structure?
- 6 Which is an example of generalized machine learning?
- 7 Is the generalized linear model a statistical or machine learning model?
- 8 Why is GLM regarded as a machine learning technique?
- 9 Which is the best description of machine learning?
What is a generalized algorithm?
The brief answer is: generalized machine algorithm is an algorithm that can do well and give good results in new data that never seen before.
What are the three types of machine learning algorithms?
Broadly speaking, Machine Learning algorithms are of three types- Supervised Learning, Unsupervised Learning, and Reinforcement Learning.
What type of algorithm is machine learning?
There are four types of machine learning algorithms: supervised, semi-supervised, unsupervised and reinforcement.
What are the components of generalization error?
Definition. Firstly, let’s define “generalization error”. Notice that the gap between predictions and observed data is induced by model inaccuracy, sampling error, and noise. Some of the errors are reducible but some are not.
What is pseudo code in data structure?
Definition: Pseudocode is an informal way of programming description that does not require any strict programming language syntax or underlying technology considerations. It is used for creating an outline or a rough draft of a program. Pseudocode summarizes a program’s flow, but excludes underlying details.
Which is an example of generalized machine learning?
Training a generalized machine learning model means, in general, it works for all subset of unseen data. An example is when we train a model to classify between dogs and cats. If the model is provided with dogs images dataset with only two breeds, it may obtain a good performance.
Is the generalized linear model a statistical or machine learning model?
Generalized Linear Models is a statistical development. However new Bayesian treatments puts this algorithm also in machine learning playground. So I believe both claims could be right, since the interpretation and treatment of how it works could be different.
Why is GLM regarded as a machine learning technique?
A perfect industrial application with GLM can explain why your friend told you that GLM was regarded as a machine learning technique . You can refer the source paper http://www.kdd.org/kdd2016/papers/files/adf0562-zhangA.pdf about that .
Which is the best description of machine learning?
Machine learning comes from a computer science perspective. The models are algorithmic and usually very few assumptions are required regarding the data. We work with hypothesis space and learning bias. The best exposition of machine learning I found is contained in Tom Mitchell’s book called Machine Learning.