代写IE 7275、代做Data Mining、代写R设计、代做R

日期：2018-12-06 09:40

Homework 6

IE 7275 Data Mining in Engineering

Note: Read the following literature before you attempt to solve the problems in this

homework.

Neural network related documents

Documentation on neuralnet.pdf

Examples on neuralnet.pdf

Reference manual on neuralnet.pdf

Documentation on rsnns.pdf (focus on mlp)

Reference manual on rsnns.pdf (focus on mlp)

Reference manual on nnet.pdf

Visualizing neural networks in R.pdf

Using neural networks for credit scoring.pdf

Lift charts related documents

Tutorial on lift charts with R.pdf

Documentation on lift.pdf

Lift charts to compare binary predictive models.pdf

Documentation on gain.pdf

Problem 1 (Car Sales, Neural Networks)

Consider the data on used cars given in ToyotaCorolla.xlsx. The data has 1436

records and details on 38 attributes, including Price, Age, KM, HP, and other

specifications. The goal is to predict the price of a used Toyota Corolla based on its

specifications using a multilayer neural network. Select appropriate predictor variables.

Use 75% of the data for training a multilayer neural network and 25% to validate the

network performance. Use the default algorithm (“rprop+” in the neuralnet package).

Record the RMS error for the training data and the validation data. Repeat the process,

changing the threshold values, 1, 0.1, 0.05, 0.01, 0.005, 0.001, and 0.0001. Set threshold

to these values.

(a) What happens to the RMS error (or Sum of Squares Error) for the training data as

the value of threshold decreases?

(b) What happens to the RMS error Sum of Squares Error for the validation data?

(c) Conduct an experiment to assess the effect of changing the number of hidden

layer nodes (default 1), e.g., 1,2,4,8.

(d) Conduct a similar experiment to assess the effect of changing the number of

layers from 1 to 2 in the network.

(e) Study the effect of gradient descent step size (learningrate) on the training

process and the network performance.

Files Included in the Folder:

1. Homework.pdf

2. Documentation on neuralnet.pdf

3. Examples on neuralnet.pdf

4. Reference manual on neuralnet.pdf

5. Documentation on rsnns.pdf (focus on mlp)

6. Reference manual on rsnns.pdf (focus on mlp)

7. Reference manual on nnet.pdf

8. Visualizing neural networks in R.pdf

9. Using neural networks for credit scoring.pdf

10. Tutorial on lift charts with R.pdf

11. Documentation on lift.pdf

12. Lift charts to compare binary predictive models.pdf

13. Documentation on gain.pdf

14. Toyta Corolla.xlsx