Uncertainty Quantification (1): Enter Conformal Predictors

00:06:43
https://www.youtube.com/watch?v=xZbuFKWV5NA

Resumen

TLDRThis video discusses uncertainty quantification in machine learning, focusing on conformal predictors. It explains the limitations of point predictions and the significance of providing prediction intervals, which include a range of possible values along with associated probabilities. The importance of ensuring that prediction intervals have validity and efficiency, even with finite data sets, is highlighted. The video outlines the desired properties of a robust uncertainty quantification method, setting the stage for future discussions on conformal predictors, which can fulfill these criteria easily.

Para llevar

  • 🔍 Uncertainty quantification is essential for understanding prediction reliability.
  • 📊 Point predictions alone are insufficient for high-stakes decisions.
  • 📈 Prediction intervals provide a range along with a probability of containing the true value.
  • ✔️ Validity of prediction intervals is crucial; they must have the claimed coverage.
  • 🔒 Finite sample validity is necessary for practical applications with limited data.
  • 🏷️ Efficiency relates to the tightness of the prediction interval.
  • 🔄 Model agnosticism allows the use of any point predictor without dependency.
  • 🌐 Conformal predictors meet key requirements for uncertainty quantification.

Cronología

  • 00:00:00 - 00:06:43

    In this video, the focus is on uncertainty quantification and the importance of using conformal predictors for enhancing predictions made by machine learning models. The discussion highlights how traditional point predictions lack an understanding of potential error margins, making it difficult to trust the predictions, especially in high-risk scenarios. The concept of providing prediction intervals, along with probabilities indicating the likelihood of the true value falling within those intervals, is introduced as a solution to gauge uncertainty effectively. The video emphasizes the necessity for these prediction intervals to have a validity property, ensuring that they maintain at least a 90% coverage probability. Additionally, it discusses finite sample validity and prediction interval efficiency, stressing that a robust method for prediction must be model-agnostic and distribution-free. Finally, it sets the stage for the next episode, which will delve into how to utilize conformal predictors for interval prediction.

Mapa mental

Vídeo de preguntas y respuestas

  • What is uncertainty quantification?

    Uncertainty quantification refers to the process of attaching a measure of uncertainty to predictions made by machine learning models.

  • Why do we need uncertainty quantification?

    It allows informed decision-making by providing insights into how reliable predictions are, based on their potential errors.

  • What is a prediction interval?

    A prediction interval is a range of values within which the true value is expected to fall, along with a specified probability.

  • What are conformal predictors?

    Conformal predictors are a class of methods for uncertainty quantification that meet several key requirements.

  • What is finite sample coverage validity?

    It ensures that the prediction intervals maintain validity even with limited data points.

  • What does efficiency mean in the context of prediction intervals?

    Efficiency refers to the tightness of the prediction interval; a tighter interval is more efficient.

  • What are the necessary properties for a good uncertainty quantification method?

    It should possess finite sample validity, efficiency, be model agnostic, and distribution free.

  • When will the next video be released?

    The next video will cover how to predict intervals using conformal predictors.

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Desplazamiento automático:
  • 00:00:00
    welcome to the second playlist of the
  • 00:00:03
    channel which will be about quantifying
  • 00:00:05
    uncertainty and predictions of an ml
  • 00:00:08
    model using a class of methods called
  • 00:00:12
    conformal predictors
  • 00:00:14
    but what is uncertainty quantification
  • 00:00:16
    and why do we need it here is how it
  • 00:00:20
    usually goes when you build a model to
  • 00:00:23
    predict something in a supervised
  • 00:00:25
    setting
  • 00:00:26
    you start with a data set consisting of
  • 00:00:29
    X Y Pairs and from there you build a
  • 00:00:33
    model now when you encounter a new data
  • 00:00:37
    point with X our let's say 0.5 and then
  • 00:00:41
    unknown through Target menu the model
  • 00:00:44
    makes a prediction but relying solely on
  • 00:00:48
    point predictions does not give us any
  • 00:00:51
    sense of how close or far that predicted
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    Point may be from the true unknown value
  • 00:00:58
    as far as the point prediction is
  • 00:01:01
    concerned the true Point can be anywhere
  • 00:01:04
    this becomes problematic especially for
  • 00:01:08
    high risk tasks as we cannot gauge how
  • 00:01:12
    much the prediction could be of the Mark
  • 00:01:14
    it's a challenge to put trust in a
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    prediction when we lack a sense of its
  • 00:01:20
    potential errors
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    if we go beyond Point predictions and
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    provide additional quantitative
  • 00:01:28
    statements about the likelihood of those
  • 00:01:31
    predictions being incorrect individuals
  • 00:01:34
    can then decide how much they want to
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    rely on these predictions based on their
  • 00:01:40
    own risk tolerance and that is where
  • 00:01:43
    uncertainty quantification comes into
  • 00:01:46
    play it empowers us to attach a measure
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    of uncertainty to our predictions
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    allowing for informed decision-making
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    tailored to our own Comfort level with
  • 00:01:58
    risk
  • 00:02:00
    let's break it down imagine the same
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    point prediction that we had earlier but
  • 00:02:06
    this time instead of providing just a
  • 00:02:09
    specific value as our prediction we use
  • 00:02:12
    the historical data to predict an
  • 00:02:15
    interval or a range of values we then
  • 00:02:19
    assign a probability to this interval
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    indicating the likelihood of the True
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    Value falling within it for instance we
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    might confidently state that there is at
  • 00:02:32
    least a 90 percent probability that the
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    true label lies within our predicted
  • 00:02:38
    interval this is often called 90
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    prediction interval in simple terms this
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    means that we do not only acknowledge
  • 00:02:47
    the possibility of being wrong in our
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    predictions but also we are quantifying
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    that possibility by stating that the
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    probability of our prediction being
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    incorrect is less than 10 percent
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    ensuring that the probabilistic
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    statement attached to a prediction
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    interval holds true is of a Paramount
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    importance for instance if we claim that
  • 00:03:13
    a prediction interval has 90 percent
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    coverage it means that the probability
  • 00:03:18
    of it containing the true value or the
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    label must be at least 90 percent this
  • 00:03:25
    is the most important property that a
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    prediction interval must satisfy because
  • 00:03:31
    not satisfying it defeats the main
  • 00:03:34
    purpose of uncertainty quantification so
  • 00:03:38
    in the next episode we will discuss in
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    detail how one can verify a claim about
  • 00:03:45
    its satisfaction
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    while validity is a crucial property of
  • 00:03:50
    predicted intervals there is another
  • 00:03:53
    aspect that we need to consider the
  • 00:03:56
    robustness probability when constructed
  • 00:03:59
    using a finite data set some methods may
  • 00:04:03
    claim validity in an asymptotic sense
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    where it holds true when constructed
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    with an effectively infinite number of
  • 00:04:12
    data points but because in practice the
  • 00:04:16
    size of our data sets are limited we
  • 00:04:19
    would like our predicted intervals to
  • 00:04:22
    have coverage validity even when
  • 00:04:24
    constructed using limited number of data
  • 00:04:28
    points in other words to have a finite
  • 00:04:30
    sample coverage validity
  • 00:04:33
    finite sample validity alone however is
  • 00:04:36
    not sufficient while a valid interval
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    tells the truth there is more to
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    consider in fact it's possible to Simply
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    inflate the integral to ensure a high
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    probability of it containing the True
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    Value this is where the second crucial
  • 00:04:54
    property comes into play efficiency
  • 00:04:57
    efficiency is directly linked to the
  • 00:05:00
    tightness of the interval the tight
  • 00:05:02
    third interval the more efficient it
  • 00:05:05
    becomes remember when it comes to
  • 00:05:07
    prediction intervals it's not just about
  • 00:05:10
    their coverage validity but also the
  • 00:05:13
    efficiency or tightness
  • 00:05:16
    for robust interval prediction we need
  • 00:05:19
    two additional properties being model
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    agnostic and distribution free a good
  • 00:05:25
    interval predictor should not be bound
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    by a specific point predictor or its
  • 00:05:31
    details or even a specific data
  • 00:05:34
    distribution in other words it should
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    not killer what model was used to make
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    the point predictions or what
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    distribution the data follows instead it
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    should seamlessly be added to any point
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    predicure treating it as a black box
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    without needing to delve into its
  • 00:05:53
    internal workings at this point we know
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    what we want from a good uncertainty
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    quantification method it needs to have a
  • 00:06:01
    finite sample validity be efficient
  • 00:06:04
    model agnostic and distribution free in
  • 00:06:08
    this playlist we will discuss one such
  • 00:06:10
    method called conformal predictors
  • 00:06:13
    the fact that they can satisfy all our
  • 00:06:16
    requirements as long as some mild
  • 00:06:19
    assumptions about the underlying data
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    are satisfied is something that sets
  • 00:06:24
    them apart from so many other
  • 00:06:26
    uncertainty quantification methods in
  • 00:06:29
    the next episode specifically we will go
  • 00:06:32
    over how to actually predict intervals
  • 00:06:35
    using conformal predictors
  • 00:06:37
    until then I hope you have enjoyed
  • 00:06:40
    watching the video see you next time
Etiquetas
  • Uncertainty Quantification
  • Conformal Predictors
  • Probability
  • Prediction Interval
  • Machine Learning
  • Model Validation
  • Risk Assessment
  • Data Prediction
  • Statistical Methods
  • Interval Prediction