Binary classification¶

Classification is about predicting an outcome from a fixed list of classes. The prediction is a probability distribution that assigns a probability to each possible outcome.

A labeled classification sample is made up of a bunch of features and a class. The class is a boolean in the case of binary classification. We'll use the phishing dataset as an example.

from river import datasets

dataset = datasets.Phishing()
dataset


Phishing websites.

This dataset contains features from web pages that are classified as phishing or not.

Name  Phishing
Samples  [1;36m1[0m,[1;36m250[0m
Features  [1;36m9[0m
Sparse  [3;91mFalse[0m
Path  [35m/home/runner/work/river/river/river/datasets/[0m[95mphishing.csv.gz[0m


This dataset is a streaming dataset which can be looped over.

for x, y in dataset:
pass


Let's take a look at the first sample.

x, y = next(iter(dataset))
x


[1m{[0m
[32m'empty_server_form_handler'[0m: [1;36m0.0[0m,
[32m'popup_window'[0m: [1;36m0.0[0m,
[32m'https'[0m: [1;36m0.0[0m,
[32m'request_from_other_domain'[0m: [1;36m0.0[0m,
[32m'anchor_from_other_domain'[0m: [1;36m0.0[0m,
[32m'is_popular'[0m: [1;36m0.5[0m,
[32m'long_url'[0m: [1;36m1.0[0m,
[32m'age_of_domain'[0m: [1;36m1[0m,
[32m'ip_in_url'[0m: [1;36m1[0m
[1m}[0m

y


[3;92mTrue[0m


A binary classifier's goal is to learn to predict a binary target y from some given features x. We'll try to do this with a logistic regression.

from river import linear_model

model = linear_model.LogisticRegression()
model.predict_proba_one(x)


[1m{[0m[3;91mFalse[0m: [1;36m0.5[0m, [3;92mTrue[0m: [1;36m0.5[0m[1m}[0m


The model hasn't been trained on any data, and therefore outputs a default probability of 50% for each class.

The model can be trained on the sample, which will update the model's state.

model = model.learn_one(x, y)


If we try to make a prediction on the same sample, we can see that the probabilities are different, because the model has learned something.

model.predict_proba_one(x)


[1m{[0m[3;91mFalse[0m: [1;36m0.494687699901455[0m, [3;92mTrue[0m: [1;36m0.505312300098545[0m[1m}[0m


Note that there is also a predict_one if you're only interested in the most likely class rather than the probability distribution.

model.predict_one(x)


[3;92mTrue[0m


Typically, an online model makes a prediction, and then learns once the ground truth reveals itself. The prediction and the ground truth can be compared to measure the model's correctness. If you have a dataset available, you can loop over it, make a prediction, update the model, and compare the model's output with the ground truth. This is called progressive validation.

from river import metrics

model = linear_model.LogisticRegression()

metric = metrics.ROCAUC()

for x, y in dataset:
y_pred = model.predict_proba_one(x)
model.learn_one(x, y)
metric.update(y, y_pred)

metric


ROCAUC: [1;36m89.36[0m%


This is a common way to evaluate an online model. In fact, there is a dedicated evaluate.progressive_val_score function that does this for you.

from river import evaluate

model = linear_model.LogisticRegression()
metric = metrics.ROCAUC()

evaluate.progressive_val_score(dataset, model, metric)


ROCAUC: [1;36m89.36[0m%


A common way to improve the performance of a logistic regression is to scale the data. This can be done by using a preprocessing.StandardScaler. In particular, we can define a pipeline to organise our model into a sequence of steps:

from river import compose
from river import preprocessing

model = compose.Pipeline(
preprocessing.StandardScaler(),
linear_model.LogisticRegression()
)

model


StandardScaler
StandardScaler ( with_std=True ) 
LogisticRegression
LogisticRegression ( optimizer=SGD ( lr=Constant ( learning_rate=0.01 ) ) loss=Log ( weight_pos=1. weight_neg=1. ) l2=0. l1=0. intercept_init=0. intercept_lr=Constant ( learning_rate=0.01 ) clip_gradient=1e+12 initializer=Zeros () ) 
metric = metrics.ROCAUC()
evaluate.progressive_val_score(dataset, model, metric)


ROCAUC: [1;36m95.07[0m%