Table of Contents
Question
You work for a large credit card company that wants to create an artificial neural network that will help predict when people are going to have trouble paying their bills. So your team gathers all the billing statements for people who had trouble paying their bills. Then you feed this data into an artificial neural network.
What is this process called?
A. This is training your artificial neural network with labeled data.
B. This is unsupervised machine learning.
C. This is classifying your data using reinforcement labels.
D. This is testing your artificial neural network with unlabeled data.
Answer
A. This is training your artificial neural network with labeled data.
Explanation
The answer is A. This is training your artificial neural network with labeled data.
In supervised machine learning, the model is trained on a dataset of labeled data. This means that each data point in the dataset has a label that tells the model what the correct output should be. In this case, the data points would be billing statements, and the labels would be whether or not the person had trouble paying their bills.
The model is then trained to learn the relationship between the input data (the billing statements) and the output data (whether or not the person had trouble paying their bills). Once the model is trained, it can be used to predict whether or not a new person will have trouble paying their bills based on their billing statement.
Here are some other examples of supervised machine learning:
- Training a model to recognize handwritten digits
- Training a model to classify images of animals
- Training a model to predict the price of a house
Unsupervised machine learning is a type of machine learning where the model is not trained on labeled data. This means that the model does not have any information about what the correct output should be. Instead, the model is trained to find patterns in the data.
Here are some examples of unsupervised machine learning:
- Clustering data points into groups
- Finding the most important features in a dataset
- Identifying outliers in a dataset
Reinforcement learning is a type of machine learning where the model learns by trial and error. The model is given a reward for taking actions that lead to desired outcomes, and it is penalized for taking actions that lead to undesired outcomes. Over time, the model learns to take actions that maximize its reward.
Here are some examples of reinforcement learning:
- Training a robot to walk
- Training a car to drive
- Training a player to play a game
Testing your artificial neural network with unlabeled data is called unsupervised testing. This is done to see how well the model can generalize to new data that it has not seen before.
Clasifying your data using reinforcement labels is not a valid process. Reinforcement learning is a type of machine learning where the model learns by trial and error, and it is not typically used for classification tasks.
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