flink-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From thvasilo <...@git.apache.org>
Subject [GitHub] flink pull request: [FLINK-2072] [ml] [docs] Add a quickstart guid...
Date Thu, 11 Jun 2015 08:01:28 GMT
Github user thvasilo commented on a diff in the pull request:

    --- Diff: docs/libs/ml/quickstart.md ---
    @@ -24,4 +25,214 @@ under the License.
     * This will be replaced by the TOC
    -Coming soon.
    +## Introduction
    +FlinkML is designed to make learning from your data a straight-forward process, abstracting
    +the complexities that usually come with having to deal with big data learning tasks.
In this
    +quick-start guide we will show just how easy it is to solve a simple supervised learning
    +using FlinkML. But first some basics, feel free to skip the next few lines if you're
    +familiar with Machine Learning (ML).
    +As defined by Murphy [1] ML deals with detecting patterns in data, and using those
    +learned patterns to make predictions about the future. We can categorize most ML algorithms
    +two major categories: Supervised and Unsupervised Learning.
    +* **Supervised Learning** deals with learning a function (mapping) from a set of inputs
    +(features) to a set of outputs. The learning is done using a *training set* of (input,
    +output) pairs that we use to approximate the mapping function. Supervised learning problems
    +further divided into classification and regression problems. In classification problems
we try to
    +predict the *class* that an example belongs to, for example whether a user is going to
click on
    +an ad or not. Regression problems one the other hand, are about predicting (real) numerical
    +values, often called the dependent variable, for example what the temperature will be
    +* **Unsupervised Learning** deals with discovering patterns and regularities in the data.
An example
    +of this would be *clustering*, where we try to discover groupings of the data from the
    +descriptive features. Unsupervised learning can also be used for feature selection, for
    +through [principal components analysis](https://en.wikipedia.org/wiki/Principal_component_analysis).
    +## Linking with FlinkML
    +In order to use FlinkML in you project, first you have to
    +[set up a Flink program](http://ci.apache.org/projects/flink/flink-docs-master/apis/programming_guide.html#linking-with-flink).
    +Next, you have to add the FlinkML dependency to the `pom.xml` of your project:
    +{% highlight xml %}
    +  <groupId>org.apache.flink</groupId>
    +  <artifactId>flink-ml</artifactId>
    +  <version>{{site.version }}</version>
    +{% endhighlight %}
    +## Loading data
    +To load data to be used with FlinkML we can use the ETL capabilities of Flink, or specialized
    +functions for formatted data, such as the LibSVM format. For supervised learning problems
it is
    +common to use the `LabeledVector` class to represent the `(features, label)` examples.
A `LabeledVector`
    --- End diff --
    Good catch, will change.

If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastructure@apache.org or file a JIRA ticket
with INFRA.

View raw message