Badges and docs (#523)

* Add badges and correct docs

* change badge color

* replace samples by observations

* remove python 3.6

README.md

@@ -1,12 +1,17 @@
 # glum
 
-![CI](https://github.com/Quantco/glm_benchmarks/workflows/CI/badge.svg)
+[![CI](https://github.com/Quantco/glm_benchmarks/workflows/CI/badge.svg)](https://github.com/Quantco/glum/actions)
+[![Docs](https://readthedocs.org/projects/pip/badge/?version=latest&style=flat)](https://glum.readthedocs.io/)
+[![Conda-forge](https://img.shields.io/conda/vn/conda-forge/glum?logoColor=white&logo=conda-forge)](https://anaconda.org/conda-forge/glum)
+[![PypiVersion](https://img.shields.io/pypi/v/glum.svg?logo=pypi&logoColor=white)](https://pypi.org/project/glum)
+[![PythonVersion](https://img.shields.io/pypi/pyversions/glum?logoColor=white&logo=python)](https://pypi.org/project/glum)
+
 
 [Documentation](https://glum.readthedocs.io/en/latest/)
 
 Generalized linear models (GLM) are a core statistical tool that include many common methods like least-squares regression, Poisson regression and logistic regression as special cases. At QuantCo, we have used GLMs in e-commerce pricing, insurance claims prediction and more. We have developed `glum`, a fast Python-first GLM library. The development was based on [a fork of scikit-learn](https://github.com/scikit-learn/scikit-learn/pull/9405), so it has a scikit-learn-like API. We are thankful for the starting point provided by Christian Lorentzen in that PR!
 
-`glum` is at least as feature-complete as existing GLM libraries like `glmnet` or `h2o`. It supports
+The goal of `glum` is to be at least as feature-complete as existing GLM libraries like `glmnet` or `h2o`. It supports
 
 * Built-in cross validation for optimal regularization, efficiently exploiting a “regularization path”
 * L1 regularization, which produces sparse and easily interpretable solutions
@@ -15,7 +20,7 @@ Generalized linear models (GLM) are a core statistical tool that include many co
 * Normal, Poisson, logistic, gamma, and Tweedie distributions, plus varied and customizable link functions
 * Box constraints, linear inequality constraints, sample weights, offsets
 
-This repo also includes tools for benchmarking GLM implementations in the `glum_benchmarks` module. For details on the benchmarking, [see here](src/glum_benchmarks/README.md). Although the performance of `glum` relative to `glmnet` and `h2o` depends on the specific problem, we find that it is consistently much faster for a wide range of problems.
+This repo also includes tools for benchmarking GLM implementations in the `glum_benchmarks` module. For details on the benchmarking, [see here](src/glum_benchmarks/README.md). Although the performance of `glum` relative to `glmnet` and `h2o` depends on the specific problem, we find that when N >> K (there are more observations than predictors), it is consistently much faster for a wide range of problems.
 
 ![](docs/_static/headline_benchmark.png)
 

docs/benchmarks.rst

@@ -5,6 +5,8 @@ The following benchmarks were run on a MacBook Pro laptop with a quad-core Intel
 
 The title of each plot refers to both which dataset the benchmark was run on and whether a L2 ridge regression penalty or an L1 lasso penalty was included. For example "Narrow-Insurance-Ridge" was run on the ``narrow-insurance`` dataset with a ridge regression penalty. Each dataset/penalty pair is tested on five distributions that cover most of the common GLM types. The outcome variable is modified appropriately so that the behavior is similar to that expected for the distribution. For example, for the Poisson regression, we predict the number of claims per person. And for the binomial regression, we predict whether any given individual has ever had a claim. For the ``housing`` dataset, we only test three distributions because it does not contain count data that can be used as an outcome.
 
+Note that glum was originally developed to solve problems where N >> K (number of observations is larger than the number of predictors), which is the case for the following benchmarks.
+
 If a bar goes out of the range of the chart, the exact runtime is printed on the bar with an arrow indicating that the bar is truncated.
 
 .. image:: _static/narrow-insurance-l2.png

setup.py

@@ -60,7 +60,6 @@
     license="BSD",
     classifiers=[  # Optional
         "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.6",
         "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",