Machine Learning
Budget: ₹600 – ₹1,500 INR
Each digit is an 8x8 pixel patch, which when reshaped is a 64 length vector.
Distance metric: the simplest distance metric for k-Nearest Neighbors is the sum of squares error between pixel values.
For each example in the test set, calculate the distance to every other example in the training set. Identify the closest k neighbors (a good use of numpy.argsort). Pick the class which most of the neighbors belong to. Break ties in any way you wish.
Compare the true class of each member of the test set to the predicted class using k-Nearest neighbors. Report the accuracy.
Now, change the value of k. Create a table with the accuracy for k=1, k=3, k=5, and k=100, and k=500
Show a classification matrix for each run of k. You can use sklearn.metrics.confusion_matrix for this
Note which errors are more common. In what way does that match your intuitions?
We need to implement one algorithm with above question either Decision trees or SVM or Random Forests
Note:Don't use in built functions.
Distance metric: the simplest distance metric for k-Nearest Neighbors is the sum of squares error between pixel values.
For each example in the test set, calculate the distance to every other example in the training set. Identify the closest k neighbors (a good use of numpy.argsort). Pick the class which most of the neighbors belong to. Break ties in any way you wish.
Compare the true class of each member of the test set to the predicted class using k-Nearest neighbors. Report the accuracy.
Now, change the value of k. Create a table with the accuracy for k=1, k=3, k=5, and k=100, and k=500
Show a classification matrix for each run of k. You can use sklearn.metrics.confusion_matrix for this
Note which errors are more common. In what way does that match your intuitions?
We need to implement one algorithm with above question either Decision trees or SVM or Random Forests
Note:Don't use in built functions.