# scikit learn – How to perform logistic lasso in python?

## scikit learn – How to perform logistic lasso in python?

The Lasso optimizes a least-square problem with a L1 penalty.

By definition you cant optimize a logistic function with the Lasso.

If you want to optimize a logistic function with a L1 penalty, you can use the `LogisticRegression`

estimator with the L1 penalty:

```
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
log = LogisticRegression(penalty=l1, solver=liblinear)
log.fit(X, y)
```

Note that only the LIBLINEAR and SAGA (added in v0.19) solvers handle the L1 penalty.

You can use glment in Python. Glmnet uses warm starts and active-set convergence so it is extremely efficient. Those techniques make glment faster than other lasso implementations. You can download it from https://web.stanford.edu/~hastie/glmnet_python/

#### scikit learn – How to perform logistic lasso in python?

# 1 scikit-learn: `sklearn.linear_model.LogisticRegression`

`sklearn.linear_model.LogisticRegression`

from scikit-learn is probably the best:

as @TomDLT said, `Lasso`

is for the least squares (regression) case, not logistic (classification).

```
from sklearn.linear_model import LogisticRegression
model = LogisticRegression(
penalty=l1,
solver=saga, # or liblinear
C=regularization_strength)
model.fit(x, y)
```

# 2 python-glmnet: `glmnet.LogitNet`

You can also use Civis Analytics python-glmnet library. This implements the scikit-learn `BaseEstimator`

API:

```
# source: https://github.com/civisanalytics/python-glmnet#regularized-logistic-regression
from glmnet import LogitNet
m = LogitNet(
alpha=1, # 0 <= alpha <= 1, 0 for ridge, 1 for lasso
)
m = m.fit(x, y)
```

Im not sure how to adjust the penalty with `LogitNet`

, but Ill let you figure that out.

# 3 other

### PyMC

you can also take a fully bayesian approach. rather than use L1-penalized optimization to find a point estimate for your coefficients, you can approximate the distribution of your coefficients given your data. this gives you the same answer as L1-penalized maximum likelihood estimation if you use a Laplace prior for your coefficients. the Laplace prior induces sparsity.

the PyMC folks have a tutorial here on setting something like that up. good luck.