The correct answer is C. You are overfitting.
Overfitting is a common problem in machine learning, and it can occur when a model learns the training data too well and is unable to generalize to new data. This can happen when the model is too complex, or when the training data is not representative of the data that the model will be used on.
In the case of a decision tree, overfitting can occur when the tree is too deep. A deep tree will have many branches, and each branch will represent a different decision that the model can make. If the tree is too deep, it may learn to make decisions that are specific to the training data, and these decisions may not be applicable to new data.
To avoid overfitting, you can try to reduce the complexity of your model, or you can try to collect more training data. You can also try to use a different machine learning algorithm, such as a random forest, which is less likely to overfit.
Here is a brief explanation of each option:
- A. Your decision trees are too shallow. This is a possible explanation, but it is less likely than overfitting. A shallow tree will have fewer branches, and each branch will represent a less complex decision. This makes it less likely that the tree will learn to make decisions that are specific to the training data.
- B. You need to increase the learning rate. This is not a likely explanation. The learning rate is a parameter that controls how quickly the model learns. Increasing the learning rate can help the model to learn more quickly, but it can also make it more likely to overfit.
- C. You are overfitting. This is the most likely explanation. Overfitting is a common problem in machine learning, and it can occur when a model learns the training data too well and is unable to generalize to new data.
- D. Incorrect data. This is also a possible explanation, but it is less likely than overfitting. Incorrect data can cause the model to learn the wrong things, but it is less likely to cause the model to learn nothing at all.