# 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
Task Binary classification
Samples 1,250
Features 9
Sparse False
Path /Users/max/projects/online-ml/river/river/datasets/phishing.csv.gz
```

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
```

```
{'empty_server_form_handler': 0.0,
'popup_window': 0.0,
'https': 0.0,
'request_from_other_domain': 0.0,
'anchor_from_other_domain': 0.0,
'is_popular': 0.5,
'long_url': 1.0,
'age_of_domain': 1,
'ip_in_url': 1}
```

```
y
```

```
True
```

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)
```

```
{False: 0.5, True: 0.5}
```

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.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)
```

```
{False: 0.494687699901455, True: 0.505312300098545}
```

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)
```

```
True
```

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: 89.36%
```

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: 89.36%
```

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: 95.07%
```