What is flexible statistical learning method?

What is flexible statistical learning method?

A flexible method will fit the data closer and with the large sample size, would perform better than an inflexible approach. The number of predictors p is extremely large, and the number of observations n is small? Worse. A flexible method would overfit the small number of observations.

Is linear regression a flexible method?

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 is flexibility in regression?

Flexible regression is a new method for performing a nonparametric multiple regression while relaxing several of the standard assumptions of regression. The approach is based on nonparametric density estimation, which results in a more synergistic and less parametrically constrained method of analysis.

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What are the advantages and disadvantages of very flexible vs less flexible approach for regression or classification?

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.

Are models flexible?

All models have a degree of flexibility and care needs to be exercised in the selection of a model or when adjusting model parameters.

What is test MSE?

In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value.

What is the meaning of flexibility *?

the ability to bend easily or without breaking:The shoe soles are made from elastic materials to provide perfect flexibility. the quality of being easily adapted or of offering many different options: This blogging platform is becoming more and more popular because of its flexibility, power, and simplicity.

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What happens when you increase the flexibility of a machine learning model?

Increasing the flexibility will reduce the bias which will decrease the error. This is unlikely to happen, since your model will always have a bias towards any quirks in your training set, and thus perform better on that set most of the time.

In what circumstances a very flexible model is more preferred?

A more flexible approach is preferable when the system is underfitted, or when the data has non-linear characteristcs. A less flexible approach is preferable when the dataset has few observations, or when more interpretability is desirable, or when the data tends to a linear behavior.

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.

What is flexible supply chain?

Flexible supply chains means that a company can easily adjust production levels, raw-material purchases, and transport capacity in order to maximize profits. When demand is high the company boost production, when it is low it can scale back.

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What is the difference between RMSE and MSE?

MSE (Mean Squared Error) represents the difference between the original and predicted values which are extracted by squaring the average difference over the data set. It is a measure of how close a fitted line is to actual data points. RMSE (Root Mean Squared Error) is the error rate by the square root of MSE.