Description
Here’s a hint to figure out how to interpret the confusion matrix. After splitting the data into train and test, execute this code. (Be sure to comment this out before turning in your project!) If the number of mines and rocks in the test data differ, you can look at your confusion matrix and easily determine which row is for rocks and which is for mines.rocks = 0 # initialize countersmines = 0for obj in y_test: # for all of the objects in the test set if obj == 2: # mines are class 2, rocks are class 1 mines += 1 # increment the appropriate counter else: rocks += 1print(“rocks”,rocks,” mines”,mines) # print the results..i shared a link for the the data to use as I’m unable to upload it. https://drive.google.com/file/d/1YrWWoSpUZruCL02LmVVB6HDYP56SsUiq
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Project: Machine Learning Mine Versus Rock
You are the sonar operator on the SeaView
Naval submarine. The path of your next
mission will take you through an old mine
field that has never been cleared. You know
that your improbable Cobra shaped
submarine is barely sea worthy and cannot
stand a mine explosion. Experience tells you
mines blowing up within 30 meters of the
ship are a problem. These mines use
magnetic detection to tell when a ship is near. You are concerned because the SeaView is particular
vulnerable, and there is no good way to escape a submarine if it is stricken by a mine.
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Given all this motivation you have some training data that may allow you to detect the difference
between the mines and surrounding rocks. It is critical that you detect as many mines as possible. Your
test data is in file sonar_all_data_2.csv, available in the project assignment. There you will find multiple
observations of sonar data for mines and rocks each with 60 time samples which either indicate the
presence of a mine (actually a hollow pipe) or a rock (actually a rock).
Split the data to train using 70% of the data set to detect a mine versus a rock and the apply this to the
30% test dataset, as usual. You will use a neural network to train and test. However, this project will
focus on PCA analysis. Perform learning on the data set using 1 through 60 principal components. That
is, use just the top component, then the top two, then the top three, and so on. Determine the number
of components that yields the best results on the test data.
To create a neural network, use the MLPClassifier, which is a multi-level Perceptron. You may want to
start with something like this:
model = MLPClassifier( hidden_layer_sizes=(100), activation=’logistic’, max_iter=2000, alpha=0.00001,
solver=’adam’, tol=0.0001 )
Feel free to modify the parameters or add others to tune your results.
Each time you run, the above will start with a different random seed. How much difference does this
make to your results? Is it better to pick a random seed and use that seed for each number of
components and then try again with a different seed? (random_state=new_seed is how you set the
seed.)
It is likely that you will have convergence issues that will result in warnings. To suppress the warnings,
use this code:
from warnings import filterwarnings
filterwarnings(‘ignore’)
To create a confusion matrix:
from sklearn.metrics import confusion_matrix
cmat = confusion_matrix(actual,predicted)
Deliverables:
1) Your python code which should be well organized and documented and in a file called proj2.py.
2) For each number of components used, print the number of components and the accuracy
achieved. (Use test accuracy.)
3) At the end, print the maximum accuracy along with the number of components that achieved
that accuracy.
4) Plot accuracy versus the number of components.
5) Print the confusion matrix which results from the analysis which resulted in the maximum
accuracy.
6) Write a one paragraph conclusion of this work containing reference to results, and conclusions
you draw about your chances of surviving a real mine field. What does the confusion matrix tell
you? In addition, discuss why that number of components was the maximum as opposed to
some other number and the general shape of the plot you created. Finally, explain how you
chose the parameters for the MLPClassifier. This paragraph should be in a file called proj2.pdf.
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