Contents
What is flexible model in machine learning?
A flexible model allows you to take full advantage of a large sample size (large n). A flexible model will be necessary to find the nonlinear effect. A flexible model will cause you to fit too much of the noise in the problem (when variance of the error terms is high).
What is a flexible model?
Model Flexibility The flexibility of a model can be described as how much is model’s behavior influenced by characteristics of the data; therefore, the flexibility or inflexibility of a model is a characteristic that should not be overlooked. It can mean the difference between a useful or useless tool.
What are the advantages and disadvantages of a very flexible model versus less flexible?
The advantages of a very flexible approach are that it may give a better fit for non-linear models and it decreases the bias. The disadvantages of a very flexible approach are that it requires estimating a greater number of parameters, it follows the noise too closely (overfit) and it increases the variance.
Is linear regression a flexible approach?
A linear regression is said to be be inflexible. On the other hand, if you have 9 training sets that are each very different, and you require a more rigid decision boundary, the model will be deemed flexible, just because the model can’t be a straight line.
What are the disadvantages of a very flexible approach for regression and classification models?
The disadvantages for a very flexible approach for regression or classification are requires estimating a greater number of parameters, follow the noise too closely (overfit), increasing variance.
How do you build a flexible supply chain?
5 Steps to Building a More Flexible Supply Chain
- Diversify Suppliers. Investing in building supply chain infrastructure that can withstand major global events begins with a diversified supplier base.
- Collaborate On Alternative Routes.
- Create Joint Business Planning Processes.
- Incorporate Automation on Factory Floors.