Python机器学习项目

As a field, machine learning is closely related to computational statistics,
so
 having
 a
 background
 knowledge
 in
 statistics
 is
 useful for
understanding and leveraging machine learning algorithms.
For those who may not have studied statistics, it can be helpful to first
define correlation and regression, as they are commonly used techniques
for
 investigating
 the
 relationship
 among
 quantitative variables.
Correlation is a measure of association between two variables that are not
designated as either dependent or independent. Regression at a basic
level is used to examine the relationship between one dependent and one
independent variable. Because regression statistics can be used to
anticipate the dependent variable when the independent variable is
known, regression enables prediction capabilities.
Approaches to machine learning are continuously being developed.
For our purposes, we’ll go through a few of the popular approaches that
are being used in machine learning at the time of writing.
k-nearest neighbor
The k-nearest neighbor algorithm is a pattern recognition model that can
be used for classification as well as regression. Often abbreviated as k-
NN, the k in k-nearest neighbor is a positive integer, which is typically
small. In either classification or regression, the input will consist of the k
closest training examples within a space.
We will focus on k-NN classification. In this method, the output is class
membership. This will assign a new object to the class most common
among its k nearest neighbors. In the case of k = 1, the object is assigned
to the class of the single nearest neighbor.
Let’s look at an example of k-nearest neighbor. In the diagram below,
there are blue diamond objects and orange star objects. These belong to
two separate classes: the diamond class and the star class.
k-nearest neighbor initial data set
When a new object is added to the space — in this case a green heart —
we will want the machine learning algorithm to classify the heart to a
certain class.
k-nearest neighbor data set with new object to classify
When we choose k = 3, the algorithm will find the three nearest
neighbors of the green heart in order to classify it to either the diamond
class or the star class.
In our diagram, the three nearest neighbors of the green heart are one
diamond and two stars. Therefore, the algorithm will classify the heart
with the star class.
k-nearest neighbor data set with classification complete
Among the most basic of machine learning algorithms, k-nearest
neighbor is considered to be a type of “lazy learning” as generalization
beyond the training data does not occur until a query is made to the
system.
Decision Tree Learning
For general use, decision trees are employed to visually represent
decisions and show or inform decision making. When working with
machine learning and data mining, decision trees are used as a predictive
model. These models map observations about data to conclusions about
the data’s target value.
The goal of decision tree learning is to create a model that will predict
the value of a target based on input variables.
In the predictive model, the data’s attributes that are determined
through observation are represented by the branches, while the
conclusions about the data’s target value are represented in the leaves.
When “learning” a tree, the source data is divided into subsets based
on an attribute value test, which is repeated on each of the derived
subsets recursively. Once the subset at a node has the equivalent value as
its target value has, the recursion process will be complete.
Let’s look at an example of various conditions that can determine
whether or not someone should go fishing. This includes weather
conditions as well as barometric pressure conditions.
fishing decision tree example
In the simplified decision tree above, an example is classified by
sorting it through the tree to the appropriate leaf node. This then returns
the classification associated with the particular leaf, which in this case is
either a Yes or a No. The tree classifies a day’s conditions based on
whether or not it is suitable for going fishing.
A true classification tree data set would have a lot more features than
what is outlined above, but relationships should be straightforward to
determine.
 When
 working
 with
 decision
 tree learning,
 several
determinations need to be made, including what features to choose, what
conditions to use for splitting, and understanding when the decision tree
has reached a clear ending.
Deep Learning
Deep learning attempts to imitate how the human brain can process light
and sound stimuli into vision and hearing. A deep learning architecture
is inspired by biological neural networks and consists of multiple layers
in an artificial neural network made up of hardware and GPUs.
Deep learning uses a cascade of nonlinear processing unit layers in
order to extract or transform features (or representations) of the data. The
output of one layer serves as the input of the successive layer. In deep
learning, algorithms can be either supervised and serve to classify data,
or unsupervised and perform pattern analysis.
Among the machine learning algorithms that are currently being used
and developed, deep learning absorbs the most data and has been able to
beat humans in some cognitive tasks. Because of these attributes, deep
learning has become the approach with significant potential in the
artificial intelligence space
Computer vision and speech recognition have both realized significant
advances from deep learning approaches. IBM Watson is a well-known
example of a system that leverages deep learning.
Human Biases
Although data and computational analysis may make us think that we
are receiving objective information, this is not the case; being based on
data does not mean that machine learning outputs are neutral. Human
bias plays a role in how data is collected, organized, and ultimately in the
algorithms that determine how machine learning will interact with that
data.
If, for example, people are providing images for “fish” as data to train
an algorithm, and these people overwhelmingly select images of
goldfish, a computer may not classify a shark as a fish. This would create
a bias against sharks as fish, and sharks would not be counted as fish.
When using historical photographs of scientists as training data, a
computer may not properly classify scientists who are also people of
color or women. In fact, recent peer-reviewed research has indicated that
AI and machine learning programs exhibit human-like biases that
include race and gender prejudices. See, for example “Semantics derived
automatically from language corpora contain human-like biases” and
“Men Also Like Shopping: Reducing Gender Bias Amplification using
Corpus-level Constraints” [PDF].
As machine learning is increasingly leveraged in business, uncaught
biases can perpetuate systemic issues that may prevent people from
qualifying for loans, from being shown ads for high-paying job
opportunities, or from receiving same-day delivery options.
Because human bias can negatively impact others, it is extremely
important to be aware of it, and to also work towards eliminating it as
much as possible. One way to work towards achieving this is by ensuring
that there are diverse people working on a project and that diverse
people are testing and reviewing it. Others have called for regulatory
third parties to monitor and audit algorithms, building alternative
systems that can detect biases, and ethics reviews as part of data science
project planning. Raising awareness about biases, being mindful of our
own unconscious biases, and structuring equity in our machine learning
projects and pipelines can work to combat bias in this field.