COMPSCI 1MD3 Lecture Notes - Lecture 22: Scipy, Data Element, Feature Vector
9 Apr 2018
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Goal: - come up with a rule that classifies inputs based on the classes in the training. We can first convert each animal"s features into a feature vector, with some value representing each feature. Is for classification of text and of more general data in python. To use this, you need to install it yourself. Pip3 install numpy scipy scikit-learn python3 -m pip install numpy scipy scikit-learn sudo pip3 install numpy scipy scikit-learn. Construct feature vectors for the labelled data a. For nltk we need a list, where each element of the list is a tuple containing a dictionary of feature names and values, and a label. Divide the data into two sets: a training set and a testing set. Train a classifier on the training data to develop a model. Use the classifier to predict the labels for the testing data we can run the classifier on a feature vector to see what it predicts.
