The ZeroR Classifier .. What it is and How it Works

00:05:35
https://www.youtube.com/watch?v=kUbYN4AcPmA

摘要

TLDRThe Zero R classifier is a simple classification method that ignores all predictors and only focuses on the target variable, predicting the majority class based on its frequency. This classifier is useful for establishing a baseline performance, indicating the least accurate prediction model. The video uses a weather dataset example to illustrate how the Zero R classifier predicts outcomes by counting class occurrences and shows how to evaluate its performance using a confusion matrix.

心得

  • 🔍 Zero R focuses only on the target variable.
  • 📊 It builds a frequency table of the target.
  • 🔑 It predicts the majority class for new inputs.
  • 📉 There's no predictive power, but it sets a baseline.
  • ⚖️ Useful for benchmarking other classifiers.
  • 📈 Constructs metrics from a confusion matrix.
  • 📅 Example uses a weather dataset.
  • 💡 Categorical data makes frequency tables easy.
  • 🌟 Accuracy metric from Zero R may be low.
  • 🔄 Zero R can be applied to numerical data by transformation.

时间轴

  • 00:00:00 - 00:05:35

    The Zero R classifier, named for its reliance on zero rules, only considers the target class and ignores all predictors or features. It predicts the majority class based on a frequency table of the target variable, making it a baseline classifier for measuring the performance of other models. In a weather dataset example, Zero R predicts the class based on the majority outcome (like 'yes' for play or 'no'). The accuracy of this model is calculated through a confusion matrix, which reveals it has 64% accuracy by always predicting the majority class. Zero R isn't predictive but serves as a benchmark; any model performing worse than Zero R is deemed ineffective.

思维导图

视频问答

  • What does Zero R stand for?

    Zero R stands for 'zero rules', indicating that it ignores all predictors and focuses solely on the class.

  • How does the Zero R classifier work?

    Zero R constructs a frequency table from the target variable and predicts the most frequent value.

  • What is the purpose of the Zero R classifier?

    It serves as a baseline classifier to compare the performance of other classification methods.

  • How can Zero R be applied to a dataset?

    You create a frequency table from the target variable's counts and predict future inputs based on the majority class.

  • What metrics can be derived from a confusion matrix in Zero R?

    Metrics such as accuracy, positive predictive value, negative predictive value, sensitivity, and specificity can be derived.

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  • 00:00:00
    hello again in this video I will be
  • 00:00:02
    explaining to you the idea behind the
  • 00:00:05
    Zer R classifier let's remind ourselves
  • 00:00:08
    where we are first we mentioned before
  • 00:00:11
    that z r is based on frequency table
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    likewise is the one R the naive base and
  • 00:00:18
    the decision three classifiers now the
  • 00:00:21
    zero R classifier if you just look at
  • 00:00:24
    the name zero R so zero rules zero r
  • 00:00:30
    stands for zero rules what that means
  • 00:00:33
    is that um it this classifier relies on
  • 00:00:39
    the Target and ignores all predictors if
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    you remember from the last videos we saw
  • 00:00:45
    the weather data set the weather data
  • 00:00:47
    set and we mentioned that we had four
  • 00:00:50
    predictors or four features the zero R
  • 00:00:52
    only focuses on the class and it does
  • 00:00:55
    not actually care about the predictors
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    or the features what it does is
  • 00:01:00
    it simply predicts the majority class or
  • 00:01:04
    the majority uh
  • 00:01:07
    category although there's no
  • 00:01:08
    predictability power in Zer R it's
  • 00:01:11
    useful for determining a baseline
  • 00:01:13
    performance or
  • 00:01:14
    Baseline uh
  • 00:01:16
    classification that Baseline classifier
  • 00:01:18
    can be used as a benchmark for other
  • 00:01:20
    classification methods so by a baseline
  • 00:01:22
    here we means this is the least accurate
  • 00:01:25
    classifier that we can have if we
  • 00:01:27
    develop a model and it's accuracy is
  • 00:01:30
    worse than this then the model is
  • 00:01:33
    useless now the way it works it
  • 00:01:36
    constructs a frequency table for the
  • 00:01:38
    Target and select its most frequent
  • 00:01:42
    value what that means is we ignore the
  • 00:01:45
    other features we only look at the class
  • 00:01:48
    we build a frequency table from the
  • 00:01:49
    class and for any new input we always
  • 00:01:53
    predict predicted to be uh as the
  • 00:01:56
    majority of the classes from the class
  • 00:01:59
    column I'm going to show you an example
  • 00:02:01
    and things will make uh
  • 00:02:04
    sense um now if we look at the weather
  • 00:02:08
    data we've seen this before we said we
  • 00:02:10
    have four predictors or four features
  • 00:02:13
    and the fifth column here is our class
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    either to play or not to play yes or no
  • 00:02:18
    now the zero what it does is as we
  • 00:02:19
    mentioned before it ignores all of these
  • 00:02:22
    predictors or features and it only
  • 00:02:24
    builds a a frequency table from the
  • 00:02:27
    target hopefully you are familiar with
  • 00:02:28
    what a frequency table is it just to
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    count basically so here for example we
  • 00:02:32
    only count how many yeses and how many
  • 00:02:33
    NOS we have if we have more than two
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    classes then uh we just count them as
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    well so we only have nine yeses and two
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    NOS here so I'm sorry nine yeses and
  • 00:02:44
    five NOS we have 14 instances or 14
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    observations as you can see the number
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    of
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    instances and now for any future input
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    it will always be guessed to be of type
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    yes or of class yes what that means is
  • 00:03:01
    if we have any input now for example a
  • 00:03:04
    new input of uh Outlook uh rainy
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    temperature hot uh humidity normal windy
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    true and we want to guess whether to
  • 00:03:15
    play or not yes or no then the zero I
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    will always guess that to be a yes
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    because that's the majority class now
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    from this frequency table from this data
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    we can easily build a confusion Matrix
  • 00:03:28
    to evaluate the performance
  • 00:03:30
    again if you're not familiar with what a
  • 00:03:33
    uh confusion Matrix is then please go
  • 00:03:35
    back and watch my uh model evaluation
  • 00:03:38
    tutorial there I explain in detail and
  • 00:03:40
    give examples on how to construct it and
  • 00:03:42
    how to interpret it and how to extract
  • 00:03:45
    useful metrics from it now uh for our
  • 00:03:49
    classifier now because we predict
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    everything to be a
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    yes now we have the actual classes yes
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    or no the counts of these classes and
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    the actual counts of the predicted
  • 00:04:01
    classes and we have nine yeses because
  • 00:04:04
    we have 14 points now and they will all
  • 00:04:07
    be classified as a yes then we have nine
  • 00:04:10
    as our true positive actually yes
  • 00:04:13
    predicted to be yes and five as uh um
  • 00:04:16
    false positives actually no but
  • 00:04:19
    predicted to be a yes now we can uh uh
  • 00:04:22
    use the equations we explained in our
  • 00:04:26
    um uh uh model evaluation tutorial to
  • 00:04:30
    extract these metrics positive
  • 00:04:32
    predictive value negative predictive
  • 00:04:33
    value sensitivity specificity and the
  • 00:04:36
    accuracy and you notice now the accuracy
  • 00:04:38
    now is
  • 00:04:40
    64 just to repeat we can build confusion
  • 00:04:43
    Matrix and get some metrics and ZR is
  • 00:04:48
    only useful for determining a baseline
  • 00:04:50
    performance for other classification
  • 00:04:52
    methods going back to the data set you
  • 00:04:54
    can see here our variables now are all
  • 00:04:57
    categorical this is why it's quite easy
  • 00:05:00
    even classic categorical is very easy to
  • 00:05:02
    build um uh a frequency table here we
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    don't use predictors but if your
  • 00:05:08
    classifier use use the predictors and
  • 00:05:10
    your data and the classifier is based on
  • 00:05:13
    frequency tables and your data is
  • 00:05:14
    numerical then it's quite easy to
  • 00:05:16
    transform it into categorical again it's
  • 00:05:19
    quite easy to transform numerical data
  • 00:05:21
    into categorical and the other way
  • 00:05:23
    around if you're not familiar with this
  • 00:05:25
    then please watch my data exploration
  • 00:05:27
    and Analysis tutorial I'm going to stop
  • 00:05:29
    here zero R zero R classifier nice and
  • 00:05:33
    simple thanks for watching and I'll see
  • 00:05:34
    you next time
标签
  • Zero R
  • classifier
  • baseline performance
  • frequency table
  • majority class
  • confusion matrix
  • classification metrics
  • data analysis
  • predictive model
  • weather dataset