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Not only in Machine Learning but also in general life, especially business life, you will hear questiones like "How accurate is your product?" or "How precise is your machine?". When people get replies like "This is the most accurate product in its field!" or "This machine has the highest imaginable precision!", they feel fomforted by both answers. Shouldn't they? Indeed, the terms accurate and precise are very often used interchangeably. We will give exact definitions later in the text, but in a nutshell, we can say: Accuracy is a measure for the closeness of some measurements to a specific value, while precision is the closeness of the measurements to each other. These terms are also of extreme importance in Machine Learning (ML). We need them for evaluating ML algorithms or better their results.
We will present in this chapter of our Python Machine Learning Tutorial four important metrics. These metrics are used to evaluate the results of classifications. The metrics are:
- Accuracy
- Precision
- Recall
- F1-Score
We will introduce each of these metrics and we will discuss the pro and cons of each of them. Each metric measures something different about a classifiers performance. The metrics will be of outmost importance for all the chapters of our Machine Learning tutorial.
Machine Learning Terminology
Representation and Visualization of Data
Loading the Iris Data with Scikit-learn
Visualising the Features of the Iris Data Set
Scatterplot 'Matrices
Datasets in sklearn
Loading Digits Data
Reading the data and conversion back into 'data' and 'labels'.
Other Interesting Distributions
k-Nearest-Neighbor Classifier
From Dividing Lines to Neural Networks
Neural Networks, Structure, Weights and Matrices
Running a Neural Network with Python
Backpropagation in Neural Networks
Training a Neural Network with Python
Softmax as Activation Function
Confusion Matrix
Neural Network
Multiple Runs
With Bias Nodes
Networks with multiple hidden layers
Networks with multiple hidden layers and Epochs
A Neural Network for the Digits Dataset
Naive Bayes Classifier with Scikit
Regression Trees
The maths behind regression trees
Regression Decision Trees from scratch in Python
Regression Trees in sklearn
TensorFlow