Where should we apply active learning?
On a certain planet has different fruits of different sizes (1-5), some of them are poisonous, while others are not. The only criterion for deciding whether a fruit is poisonous or not is its size. Our task — prepare a classifier that predicts whether a given fruit is poisonous or not. The only information we have is that size 1 fruits are not poisonous, size 5 fruits are poisonous, and after a certain size all fruits are poisonous.
The first approach is to check each fruit size, which is time and resource consuming.
Second approach — apply binary search and find the transition point (solution boundary). This approach uses less data and gives the same results as linear search.
General Algorithm: 1. train classifier with the initial training dataset 2.calculate the accuracy 3. while (accuracy & lt; desired accuracy): 4.select the most valuable data points (in general points close to decision boundary) 5.query that data point / s (ask for a label) from human oracle 6.add that data point / s to our initial training dataset 7.re-train the model 8.re-calculate the accuracy
Suitable for active learning algorithm
1. Synthesis Query
Here is an active learning model that solves valuable questions based on the likelihood of a point in the classroom. In here .
Output: Accuracy by active model: 80.7 Accuracy by random sampling: 79.5
There are several models for choosing the most valuable glasses. Some of them are: