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Tutorial Topic

This tutorial aims to provide an introduction to machine learning and scikit-learn "from the ground up". We will start with basic concepts of machine learning and implementing these using scikit-learn. Going in detail through the characteristics of several methods, we will discuss how to pick an algorithm for your application, how to set its parameters, and how to evaluate performance.

Please provide a more detailed abstract of your tutorial (again, see last years tutorials).

Machine learning is the task of extracting knowledge from data, often with the goal to generalize to new, unseen data. Applications of machine learning now touch nearly every aspect of everyday life, from the face detection in our

amueller

from sklearn.cluster import KMeans
from time import time
from sklearn.datasets import load_digits, fetch_mldata, load_iris, fetch_20newsgroups_vectorized

def bench_kmeans(data, n_clusters=5, init='random', n_init=1):
    start = time()
    km1 = KMeans(algorithm='lloyd', n_clusters=n_clusters, random_state=0, init=init, n_init=n_init).fit(X)
    print("lloyd time: %f inertia: %f" % (time() - start, km1.inertia_))
    start = time()
    km2 = KMeans(algorithm='elkan', n_clusters=n_clusters, random_state=0, init=init, n_init=n_init).fit(X)

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from colorspacious import cspace_convert
from scipy.sparse.csgraph import minimum_spanning_tree
from sklearn.metrics import euclidean_distances
import scipy.sparse as sp

from colorspacious import cspace_convert
from scipy.sparse.csgraph import minimum_spanning_tree
from sklearn.metrics import euclidean_distances
import scipy.sparse as sp

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from sklearn.base import BaseEstimator

def piper(self, other):
    from sklearn.pipeline import make_pipeline, Pipeline
    if isinstance(self, Pipeline):
        steps = ([estimator for (name, estimator) in self.steps] + [other])
        return make_pipeline(*steps)
    else:
        return make_pipeline(self, other)

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import numpy as np
import matplotlib.pyplot as plt

class Curve(object):
    def __init__(self, scores, to="B+", std_adjust=0):
        self.to = to
        self.scores = scores
        self.letters = ["A+", "A", "A-", "B+", "B", "B-", "C+", "C", "C-", "D", "F"]
        idx = self.letters.index(to)
        # +3 is because we do D and F manually

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class Formatter(object):
    def __init__(self, indent_est='step'):
        self.indent_est = indent_est
        self.types = {}
        self.htchar = ' '
        self.lfchar = '\n'
        self.indent = 0
        self.step = 4
        self.width = 79
        self.set_formater(object, self.__class__.format_object)

amueller

"""
Benchmarks np.bincount method vs np.mean for feature agglomeration in
../sklearn/cluster/_feature_agglomeration. Use of np.bincount provides
a significant speed up if the pooling function is np.mean.

np.bincount performs better especially as the size of X and n_clusters
increase.
"""
import matplotlib.pyplot as plt
import numpy as np