How to Train to the Test Set in Machine Learning

Training to the test set is a type of overfitting where a model is prepared that intentionally achieves good performance on a given test set at the expense of increased generalization error.

It is a type of overfitting that is common in machine learning competitions where a complete training dataset is provided and where only the input portion of a test set is provided. One approach to training to the test set involves constructing a training set that most resembles the test set and then using it as the basis for training a model. The model is expected to have better performance on the test set, but most likely worse performance on the training dataset and on any new data in the future.

Although overfitting the test set is not desirable, it can be interesting to explore as a thought experiment and provide more insight into both machine learning competitions and avoiding overfitting generally.

In this tutorial, you will discover how to intentionally train to the test set for classification and regression problems.

After completing this tutorial, you will know:

Training to the test set is a type of data leakage that may occur in machine learning competitions.
One approach to training to the test set involves creating a training dataset that is most similar to a provided test set.
How to use a KNN model to construct a training dataset and train to the test set with a real dataset.

Kick-start your project with my new book Data Preparation for Machine Learning, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

#data preparation #machine-learning

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How to Train to the Test Set in Machine Learning

Hermann Frami

1651383480

A Simple Wrapper Around Amplify AppSync Simulator

This serverless plugin is a wrapper for amplify-appsync-simulator made for testing AppSync APIs built with serverless-appsync-plugin.

Install

npm install serverless-appsync-simulator
# or
yarn add serverless-appsync-simulator

Usage

This plugin relies on your serverless yml file and on the serverless-offline plugin.

plugins:
  - serverless-dynamodb-local # only if you need dynamodb resolvers and you don't have an external dynamodb
  - serverless-appsync-simulator
  - serverless-offline

Note: Order is important serverless-appsync-simulator must go before serverless-offline

To start the simulator, run the following command:

sls offline start

You should see in the logs something like:

...
Serverless: AppSync endpoint: http://localhost:20002/graphql
Serverless: GraphiQl: http://localhost:20002
...

Configuration

Put options under custom.appsync-simulator in your serverless.yml file

| option | default | description | | ------------------------ | -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------- | | apiKey | 0123456789 | When using API_KEY as authentication type, the key to authenticate to the endpoint. | | port | 20002 | AppSync operations port; if using multiple APIs, the value of this option will be used as a starting point, and each other API will have a port of lastPort + 10 (e.g. 20002, 20012, 20022, etc.) | | wsPort | 20003 | AppSync subscriptions port; if using multiple APIs, the value of this option will be used as a starting point, and each other API will have a port of lastPort + 10 (e.g. 20003, 20013, 20023, etc.) | | location | . (base directory) | Location of the lambda functions handlers. | | refMap | {} | A mapping of resource resolutions for the Ref function | | getAttMap | {} | A mapping of resource resolutions for the GetAtt function | | importValueMap | {} | A mapping of resource resolutions for the ImportValue function | | functions | {} | A mapping of external functions for providing invoke url for external fucntions | | dynamoDb.endpoint | http://localhost:8000 | Dynamodb endpoint. Specify it if you're not using serverless-dynamodb-local. Otherwise, port is taken from dynamodb-local conf | | dynamoDb.region | localhost | Dynamodb region. Specify it if you're connecting to a remote Dynamodb intance. | | dynamoDb.accessKeyId | DEFAULT_ACCESS_KEY | AWS Access Key ID to access DynamoDB | | dynamoDb.secretAccessKey | DEFAULT_SECRET | AWS Secret Key to access DynamoDB | | dynamoDb.sessionToken | DEFAULT_ACCESS_TOKEEN | AWS Session Token to access DynamoDB, only if you have temporary security credentials configured on AWS | | dynamoDb.* | | You can add every configuration accepted by DynamoDB SDK | | rds.dbName | | Name of the database | | rds.dbHost | | Database host | | rds.dbDialect | | Database dialect. Possible values (mysql | postgres) | | rds.dbUsername | | Database username | | rds.dbPassword | | Database password | | rds.dbPort | | Database port | | watch | - *.graphql
- *.vtl | Array of glob patterns to watch for hot-reloading. |

Example:

custom:
  appsync-simulator:
    location: '.webpack/service' # use webpack build directory
    dynamoDb:
      endpoint: 'http://my-custom-dynamo:8000'

Hot-reloading

By default, the simulator will hot-relad when changes to *.graphql or *.vtl files are detected. Changes to *.yml files are not supported (yet? - this is a Serverless Framework limitation). You will need to restart the simulator each time you change yml files.

Hot-reloading relies on watchman. Make sure it is installed on your system.

You can change the files being watched with the watch option, which is then passed to watchman as the match expression.

e.g.

custom:
  appsync-simulator:
    watch:
      - ["match", "handlers/**/*.vtl", "wholename"] # => array is interpreted as the literal match expression
      - "*.graphql"                                 # => string like this is equivalent to `["match", "*.graphql"]`

Or you can opt-out by leaving an empty array or set the option to false

Note: Functions should not require hot-reloading, unless you are using a transpiler or a bundler (such as webpack, babel or typescript), un which case you should delegate hot-reloading to that instead.

Resource CloudFormation functions resolution

This plugin supports some resources resolution from the Ref, Fn::GetAtt and Fn::ImportValue functions in your yaml file. It also supports some other Cfn functions such as Fn::Join, Fb::Sub, etc.

Note: Under the hood, this features relies on the cfn-resolver-lib package. For more info on supported cfn functions, refer to the documentation

Basic usage

You can reference resources in your functions' environment variables (that will be accessible from your lambda functions) or datasource definitions. The plugin will automatically resolve them for you.

provider:
  environment:
    BUCKET_NAME:
      Ref: MyBucket # resolves to `my-bucket-name`

resources:
  Resources:
    MyDbTable:
      Type: AWS::DynamoDB::Table
      Properties:
        TableName: myTable
      ...
    MyBucket:
      Type: AWS::S3::Bucket
      Properties:
        BucketName: my-bucket-name
    ...

# in your appsync config
dataSources:
  - type: AMAZON_DYNAMODB
    name: dynamosource
    config:
      tableName:
        Ref: MyDbTable # resolves to `myTable`

Override (or mock) values

Sometimes, some references cannot be resolved, as they come from an Output from Cloudformation; or you might want to use mocked values in your local environment.

In those cases, you can define (or override) those values using the refMap, getAttMap and importValueMap options.

refMap takes a mapping of resource name to value pairs
getAttMap takes a mapping of resource name to attribute/values pairs
importValueMap takes a mapping of import name to values pairs

Example:

custom:
  appsync-simulator:
    refMap:
      # Override `MyDbTable` resolution from the previous example.
      MyDbTable: 'mock-myTable'
    getAttMap:
      # define ElasticSearchInstance DomainName
      ElasticSearchInstance:
        DomainEndpoint: 'localhost:9200'
    importValueMap:
      other-service-api-url: 'https://other.api.url.com/graphql'

# in your appsync config
dataSources:
  - type: AMAZON_ELASTICSEARCH
    name: elasticsource
    config:
      # endpoint resolves as 'http://localhost:9200'
      endpoint:
        Fn::Join:
          - ''
          - - https://
            - Fn::GetAtt:
                - ElasticSearchInstance
                - DomainEndpoint

Key-value mock notation

In some special cases you will need to use key-value mock nottation. Good example can be case when you need to include serverless stage value (${self:provider.stage}) in the import name.

This notation can be used with all mocks - refMap, getAttMap and importValueMap

provider:
  environment:
    FINISH_ACTIVITY_FUNCTION_ARN:
      Fn::ImportValue: other-service-api-${self:provider.stage}-url

custom:
  serverless-appsync-simulator:
    importValueMap:
      - key: other-service-api-${self:provider.stage}-url
        value: 'https://other.api.url.com/graphql'

Limitations

This plugin only tries to resolve the following parts of the yml tree:

provider.environment
functions[*].environment
custom.appSync

If you have the need of resolving others, feel free to open an issue and explain your use case.

For now, the supported resources to be automatically resovled by Ref: are:

DynamoDb tables
S3 Buckets

Feel free to open a PR or an issue to extend them as well.

External functions

When a function is not defined withing the current serverless file you can still call it by providing an invoke url which should point to a REST method. Make sure you specify "get" or "post" for the method. Default is "get", but you probably want "post".

custom:
  appsync-simulator:
    functions:
      addUser:
        url: http://localhost:3016/2015-03-31/functions/addUser/invocations
        method: post
      addPost:
        url: https://jsonplaceholder.typicode.com/posts
        method: post

Supported Resolver types

This plugin supports resolvers implemented by amplify-appsync-simulator, as well as custom resolvers.

From Aws Amplify:

NONE
AWS_LAMBDA
AMAZON_DYNAMODB
PIPELINE

Implemented by this plugin

AMAZON_ELASTIC_SEARCH
HTTP
RELATIONAL_DATABASE

Relational Database

Sample VTL for a create mutation

#set( $cols = [] )
#set( $vals = [] )
#foreach( $entry in $ctx.args.input.keySet() )
  #set( $regex = "([a-z])([A-Z]+)")
  #set( $replacement = "$1_$2")
  #set( $toSnake = $entry.replaceAll($regex, $replacement).toLowerCase() )
  #set( $discard = $cols.add("$toSnake") )
  #if( $util.isBoolean($ctx.args.input[$entry]) )
      #if( $ctx.args.input[$entry] )
        #set( $discard = $vals.add("1") )
      #else
        #set( $discard = $vals.add("0") )
      #end
  #else
      #set( $discard = $vals.add("'$ctx.args.input[$entry]'") )
  #end
#end
#set( $valStr = $vals.toString().replace("[","(").replace("]",")") )
#set( $colStr = $cols.toString().replace("[","(").replace("]",")") )
#if ( $valStr.substring(0, 1) != '(' )
  #set( $valStr = "($valStr)" )
#end
#if ( $colStr.substring(0, 1) != '(' )
  #set( $colStr = "($colStr)" )
#end
{
  "version": "2018-05-29",
  "statements":   ["INSERT INTO <name-of-table> $colStr VALUES $valStr", "SELECT * FROM    <name-of-table> ORDER BY id DESC LIMIT 1"]
}

Sample VTL for an update mutation

#set( $update = "" )
#set( $equals = "=" )
#foreach( $entry in $ctx.args.input.keySet() )
  #set( $cur = $ctx.args.input[$entry] )
  #set( $regex = "([a-z])([A-Z]+)")
  #set( $replacement = "$1_$2")
  #set( $toSnake = $entry.replaceAll($regex, $replacement).toLowerCase() )
  #if( $util.isBoolean($cur) )
      #if( $cur )
        #set ( $cur = "1" )
      #else
        #set ( $cur = "0" )
      #end
  #end
  #if ( $util.isNullOrEmpty($update) )
      #set($update = "$toSnake$equals'$cur'" )
  #else
      #set($update = "$update,$toSnake$equals'$cur'" )
  #end
#end
{
  "version": "2018-05-29",
  "statements":   ["UPDATE <name-of-table> SET $update WHERE id=$ctx.args.input.id", "SELECT * FROM <name-of-table> WHERE id=$ctx.args.input.id"]
}

Sample resolver for delete mutation

{
  "version": "2018-05-29",
  "statements":   ["UPDATE <name-of-table> set deleted_at=NOW() WHERE id=$ctx.args.id", "SELECT * FROM <name-of-table> WHERE id=$ctx.args.id"]
}

Sample mutation response VTL with support for handling AWSDateTime

#set ( $index = -1)
#set ( $result = $util.parseJson($ctx.result) )
#set ( $meta = $result.sqlStatementResults[1].columnMetadata)
#foreach ($column in $meta)
    #set ($index = $index + 1)
    #if ( $column["typeName"] == "timestamptz" )
        #set ($time = $result["sqlStatementResults"][1]["records"][0][$index]["stringValue"] )
        #set ( $nowEpochMillis = $util.time.parseFormattedToEpochMilliSeconds("$time.substring(0,19)+0000", "yyyy-MM-dd HH:mm:ssZ") )
        #set ( $isoDateTime = $util.time.epochMilliSecondsToISO8601($nowEpochMillis) )
        $util.qr( $result["sqlStatementResults"][1]["records"][0][$index].put("stringValue", "$isoDateTime") )
    #end
#end
#set ( $res = $util.parseJson($util.rds.toJsonString($util.toJson($result)))[1][0] )
#set ( $response = {} )
#foreach($mapKey in $res.keySet())
    #set ( $s = $mapKey.split("_") )
    #set ( $camelCase="" )
    #set ( $isFirst=true )
    #foreach($entry in $s)
        #if ( $isFirst )
          #set ( $first = $entry.substring(0,1) )
        #else
          #set ( $first = $entry.substring(0,1).toUpperCase() )
        #end
        #set ( $isFirst=false )
        #set ( $stringLength = $entry.length() )
        #set ( $remaining = $entry.substring(1, $stringLength) )
        #set ( $camelCase = "$camelCase$first$remaining" )
    #end
    $util.qr( $response.put("$camelCase", $res[$mapKey]) )
#end
$utils.toJson($response)

Using Variable Map

Variable map support is limited and does not differentiate numbers and strings data types, please inject them directly if needed.

Will be escaped properly: null, true, and false values.

{
  "version": "2018-05-29",
  "statements":   [
    "UPDATE <name-of-table> set deleted_at=NOW() WHERE id=:ID",
    "SELECT * FROM <name-of-table> WHERE id=:ID and unix_timestamp > $ctx.args.newerThan"
  ],
  variableMap: {
    ":ID": $ctx.args.id,
##    ":TIMESTAMP": $ctx.args.newerThan -- This will be handled as a string!!!
  }
}

Requires

serverless framework
serverless-appsync-plugin
serverless-offline
serverless-dynamodb-local (when using dynamodb resolvers only)
watchman (if Hot-reloading is desactivated it is not required)

Author: Serverless-appsync
Source Code: https://github.com/serverless-appsync/serverless-appsync-simulator
License: MIT License

#serverless #sync #graphql

Ananya Gupta

1595485129

Pros and Cons of Machine Learning Language

Amid all the promotion around Big Data, we continue hearing the expression “AI”. In addition to the fact that it offers a profitable vocation, it vows to tackle issues and advantage organizations by making expectations and helping them settle on better choices. In this blog, we will gain proficiency with the Advantages and Disadvantages of Machine Learning. As we will attempt to comprehend where to utilize it and where not to utilize Machine learning.

In this article, we discuss the Pros and Cons of Machine Learning.
Each coin has two faces, each face has its property and highlights. It’s an ideal opportunity to reveal the essence of ML. An extremely integral asset that holds the possibility to reform how things work.

Pros of Machine learning

**Effectively recognizes patterns and examples **

AI can survey enormous volumes of information and find explicit patterns and examples that would not be evident to people. For example, for an online business site like Amazon, it serves to comprehend the perusing practices and buy chronicles of its clients to help oblige the correct items, arrangements, and updates pertinent to them. It utilizes the outcomes to uncover important promotions to them.

**Do you know the Applications of Machine Learning? **

No human mediation required (mechanization)

With ML, you don’t have to keep an eye on the venture at all times. Since it implies enabling machines to learn, it lets them make forecasts and improve the calculations all alone. A typical case of this is hostile to infection programming projects; they figure out how to channel new dangers as they are perceived. ML is additionally acceptable at perceiving spam.

**Constant Improvement **

As ML calculations gain understanding, they continue improving in precision and productivity. This lets them settle on better choices. Let’s assume you have to make a climate figure model. As the measure of information you have continues developing, your calculations figure out how to make increasingly exact expectations quicker.

**Taking care of multi-dimensional and multi-assortment information **

AI calculations are acceptable at taking care of information that is multi-dimensional and multi-assortment, and they can do this in unique or unsure conditions. Key Difference Between Machine Learning and Artificial Intelligence

**Wide Applications **

You could be an e-posterior or a social insurance supplier and make ML work for you. Where it applies, it holds the ability to help convey a considerably more close to home understanding to clients while additionally focusing on the correct clients.

**Cons of Machine Learning **

With every one of those points of interest to its effectiveness and ubiquity, Machine Learning isn’t great. The accompanying components serve to confine it:

1.** Information Acquisition**

AI requires monstrous informational indexes to prepare on, and these ought to be comprehensive/fair-minded, and of good quality. There can likewise be times where they should trust that new information will be created.

**Time and Resources **

ML needs sufficient opportunity to allow the calculations to learn and grow enough to satisfy their motivation with a lot of precision and pertinence. It additionally needs monstrous assets to work. This can mean extra necessities of PC power for you.
**
Likewise, see the eventual fate of Machine Learning **

**Understanding of Results **

Another significant test is the capacity to precisely decipher results produced by the calculations. You should likewise cautiously pick the calculations for your motivation.

High mistake weakness

AI is self-governing yet exceptionally powerless to mistakes. Assume you train a calculation with informational indexes sufficiently little to not be comprehensive. You end up with one-sided expectations originating from a one-sided preparing set. This prompts unessential promotions being shown to clients. On account of ML, such botches can set off a chain of mistakes that can go undetected for extensive periods. What’s more, when they do get saw, it takes very some effort to perceive the wellspring of the issue, and significantly longer to address it.

**Conclusion: **

Subsequently, we have considered the Pros and Cons of Machine Learning. Likewise, this blog causes a person to comprehend why one needs to pick AI. While Machine Learning can be unimaginably ground-breaking when utilized in the correct manners and in the correct spots (where gigantic preparing informational indexes are accessible), it unquestionably isn’t for everybody. You may likewise prefer to peruse Deep Learning Vs Machine Learning.

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sophia tondon

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5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Ananya Gupta

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AI(Artificial Intelligence): The Business Benefits of Machine Learning

Artificial intelligence has been around since a minimum of the 1950s, but it’s only within the past few years that it’s become ubiquitous. Companies we interact with every day— Amazon, Facebook, and Google—have fully embraced AI. It powers product recommendations, maps, and social media feeds.

But it’s not only the tech giants that will employ AI in their products. AI solutions are now accessible to several businesses and individuals. And it’s becoming clear that understanding and employing AI is critical for the companies of tomorrow.

What Is AI?
In the last 20 years, there are major changes in technology—notably the arrival of the mobile. But the innovation that’s on par with inventing electricity is AI.

Machine Learning
Machine learning may be a subset of AI and maybe a set of techniques that give computers the power to find out without being explicitly programmed to try to so. One example is classification, like classifying images: during a very simplistic interpretation, for instance, a computer could automatically classify pictures of apples and oranges to travel in several folders. And with more data over time, the machine will become better future scope and career oppertunity for students who want to make career in Machine Learning.

Deep Learning and Neural Networks
Deep learning may be a further subset of machine learning that permits computers to find out more complex patterns and solve more complex problems. one among the clearest applications of deep learning is in tongue processing, which powers chatbots and voice assistants like Siri. It’s the recent advent of deep learning that has particularly been driving the AI boom.

And all of those are supported neural networks, which is that the concept machines could mimic the human brain, with many layers of artificial neurons. Neural networks are powerful once they are multi-layered, with more neurons and interconnectivity. Neural networks are researched for years, but only recently has the research been pushed to the subsequent level and commercialized.

AI Business Benefits
Now that you simply have a conceptual understanding of AI and its subsets, let’s get to the guts of it: what can AI do for you and your business? We’ll explore highlights within five areas: human resources, accounting, legal, marketing and sales, and customer support.

Human Resources
Artificial intelligence poses a big opportunity in process automation. One example would be recruitment and human resources. As an example, tasks like onboarding and administration of advantages are often automated.If you want to learn deep about AI then join Artificial Intellegence class in Noida and get offer to work on live projects.

Accounting
The dutiful accountant, languishing over the bookkeeping—it’s a classic image. But now many of their services might not be needed. Many traditional bookkeeping tasks are already being performed by AI. Areas like accounts payable and receivable are taking advantage of automated data entry and categorization.

Legal
Some of the foremost fascinating advancements in AI are associated with law and legal technology. Specifically, AI can now read “legal and contractual documents to extract provisions using tongue processing.” Blue J Legal’s website touts the platform’s ability to help with employment law. The Foresight technology “analyzes data drawn from common law cases, using deep learning to get hidden patterns in previous rulings.” briefly, cases can now be analyzed much faster, insights are often drawn from across a good array of legal knowledge, and thus business decisions are often more accurate and assured.

Sales and Marketing Analytics
Analytics can now be done much more rapidly with much larger data sets because of AI. This has profound impacts on all kinds of data analysis, including business and financial decisions.

One of the quickly changing areas is marketing and sales applications. AI makes it easier to predict what a customer is probably going to shop for by learning and understanding their purchasing patterns.

Customer Support
You’ve been there. Waiting forever on a customer support line. Perhaps with a cable company or an enormous bank. Luckily, AI is close to making your life easier, if it hasn’t already.

According to the Harvard Business Review, one of the most benefits of AI is that “intelligent agents offer 24/7 customer service addressing a broad and growing array of issues from password requests to technical support questions—all within the customer’s tongue .” For customer support, a mixture of machine and deep learning can allow queries to be analyzed quicker.

Conclusion
With AI becoming ever more pervasive, having a fundamental understanding of it’s a requirement for continued business success. Whatever role you hold in your business, understanding AI may assist you to solve problems in new and innovative ways, saving time and money. Further, it’s going to assist you to build and style the products and services of the longer term.

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Nora Joy

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