Pros: High accuracy, insensitive to outliers, no assumptions about data
Cons: Computationally expensive, requires a lot of memory
Works with: Numeric values, nominal values
The first machine-learning algorithm we’ll look at is k-Nearest Neighbors (kNN). It
works like this: we have an existing set of example data, our training set. We have
labels for all of this data—we know what class each piece of the data should fall into.
When we’re given a new piece of data without a label, we compare that new piece of
data to the existing data, every piece of existing data. We then take the most similar
pieces of data (the nearest neighbors) and look at their labels. We look at the top k
most similar pieces of data from our known dataset; this is where the k comes from. (k
is an integer and it’s usua... (查看原文)
Pros: Computationally cheap to use, easy for humans to understand learned results,
missing values OK, can deal with irrelevant features
Cons: Prone to overfitting
Works with: Numeric values, nominal values (查看原文)
General approach to decision trees
1. Collect: Any method.
2. Prepare: This tree-building algorithm works only on nominal values, so any continuous values will need to be quantized.
3. Analyze: Any method. You should visually inspect the tree after it is built.
4. Train: Construct a tree data structure.
5. Test: Calculate the error rate with the learned tree.
6. Use: This can be used in any supervised learning task. Often, trees are used to
better understand the data. (查看原文)
Logistic regression
Pros: Computationally inexpensive, easy to implement, knowledge representation
easy to interpret
Cons: Prone to underfitting, may have low accuracy
Works with: Numeric values, nominal values (查看原文)
Python is popular in the scientific and financial communities as well.A number of scientific libraries such as SciPy and NumPy allow you to do vector and matrix operations. (查看原文)
k-Nearest Neighbors
Pros: High accuracy, insensitive to outliers, no assumptions about data
Cons: Computationally expensive, requires a lot of memory
Works with: Numeric values, nominal values (查看原文)
Decision trees
Pros: Computationally cheap to use, easy for humans to understand learned results, missing values OK, can deal with irrelevant features
Cons: Prone to overfitting
Works with: Numeric values, nominal values (查看原文)
