What is the K-Nearest Neighbor (KNN) Algorithm?

00:08:00
https://www.youtube.com/watch?v=b6uHw7QW_n4

Résumé

TLDRThe K Nearest Neighbors (KNN) algorithm is a simple yet popular classification and regression tool in machine learning, working on the principle that similar data points reside close to each other. In practice, KNN classifies new instances based on their proximity to existing labeled data points in a feature space defined by various attributes. The algorithm requires calculating a distance metric, with common choices being Euclidean or Manhattan distance. Choosing the optimal value of K is crucial, as it influences the model's accuracy and susceptibility to overfitting. While KNN is easy to implement and adaptable to new data, it faces challenges in scalability, high-dimensional data performance, and increased computation during classification, which can limit its effectiveness. Despite these limitations, KNN is effective for specific applications such as data preprocessing, financial forecasting, and healthcare predictions.

A retenir

  • 🔍 KNN is a classification and regression algorithm based on proximity.
  • 📏 Key metrics used include Euclidean and Manhattan distances.
  • ⚖️ The choice of K affects classification accuracy and can lead to overfitting if too low.
  • ⚠️ KNN struggles with scalability as data sets grow, becoming inefficient.
  • 📊 High-dimensional data can confuse KNN, leading to sparse points and noise.
  • 🛠️ KNN can estimate missing values through imputation, aiding data preparation.
  • 🩺 Used in healthcare for predictions related to heart attack risks and cancer.
  • 📈 Applicable in finance for stock market forecasting and trading analysis.
  • ✨ KNN's simplicity makes it an ideal choice for beginners in data science.
  • 🍏 Effectiveness depends on context; best suited for simple datasets with fewer outliers.

Chronologie

  • 00:00:00 - 00:08:00

    The video introduces the KNN (K Nearest Neighbors) algorithm, a popular classification and regression method in machine learning. It explains how KNN groups similar data points based on proximity, using a fruit dataset categorized by sweetness and crunchiness as an example to illustrate the classification process. The algorithm determines the classification of a new fruit based on the K nearest neighbors in the dataset. The video also discusses the importance of defining a distance metric and selecting the value of K. It explains that KNN is simple to implement and adaptable but highlights its drawbacks, including scalability issues and poor performance with high-dimensional data due to the curse of dimensionality. Despite its limitations, KNN is useful for tasks like data preprocessing and healthcare predictions. The video concludes by inviting viewers to like and subscribe for more content.

Carte mentale

Vidéo Q&R

  • What does KNN stand for?

    KNN stands for K Nearest Neighbors.

  • What are the main uses of KNN?

    KNN is commonly used in recommendation systems, data preprocessing, stock market forecasting, and healthcare predictions.

  • How does KNN classify new data points?

    KNN classifies new data points by checking the nearest neighbors and determining the most common class among them.

  • What metric does KNN use to measure distance?

    KNN can use various distance metrics like Euclidean distance or Manhattan distance.

  • What is the effect of the value of K?

    The K value determines how many neighbors will be considered for classification; lower K values may lead to overfitting while higher K values can smooth predictions.

  • What are some drawbacks of KNN?

    KNN doesn't scale well with large datasets, suffers from the "curse of dimensionality," and can be memory-intensive.

  • Is KNN suitable for high-dimensional data?

    KNN typically performs poorly with high-dimensional data due to sparse distance metrics.

  • What is 'missing data imputation'?

    It's a process where KNN estimates and replaces missing values based on the nearest neighbors.

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  • 00:00:00
    whether you're just getting started on
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    your journey to becoming a data
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    scientist or you've been here for years
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    you'll probably recognize the K NN
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    algorithm it stands for K nearest
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    neighbors and it's one of the most
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    popular and simplest classification and
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    regression classifiers used in machine
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    learning today as a classification
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    algorithm KNN operates on the assumption
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    that similar data points are located
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    near each other and can be grouped in
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    the same category based on their
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    proximity so let's consider an example
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    imagine we have a data set containing
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    information about different types of
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    fruit so let's visualize our fruit data
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    set here now we have each fruit
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    categorized by two things we have it
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    categorized by its sweetness that's our
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    x axis here and then on the Y AIS we are
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    classifying it by its
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    crunchiness now we've already labeled
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    some data points so we've got a a few
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    apples here apples are very crunchy and
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    somewhat sweet and then we have a few
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    oranges down here oranges are very sweet
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    not so crunchy now suppose you have a
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    new fruit that you want to classify well
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    we measure it's crunchiness and we
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    measure its sweetness and then we can
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    plot it on the graph let's say it comes
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    out maybe
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    here the K&N algorithm will then look at
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    the K nearest points on the graph to
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    this new fruit and if most of these
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    nearest points are classified as apples
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    the algorithm will classify the new
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    fruit as an apple as well how's that for
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    an Apples to Apples comparison now
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    before a classification can be made the
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    distance must be defined and there are
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    only two requirements for a KNN
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    algorithm to achieve its goal and the
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    first one is What's called the
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    distance
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    metric the distance between the query
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    point and the other data points needs to
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    be calculated fing decision boundaries
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    and partitioning query points into
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    different regions which are commonly
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    visualized using Veron diagrams which
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    kind of look like a kaleidoscope and
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    this distance serves as our distance
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    metric and can be calculated using
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    various measures such as ukian distance
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    or Manhattan distance so that's number
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    one number two is we need now need to
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    define the value of K and the K value in
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    the KNN algorithm defines how many
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    neighbors will be checked to determine
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    the classification of a specific query
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    point so for example if k equals 1 the
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    instance will be assigned to the same
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    class as its single nearest neighbor
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    choosing the right K value largely
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    depends on the input data data with more
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    outliers or noise will likely perform
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    much better with higher values of K also
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    it's recommended to choose an odd number
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    for K to minimize the chances of ties in
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    classification now just like any machine
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    learning algorithm KNN has its strengths
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    and it has its weaknesses so let's take
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    a look at some of those and on the plus
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    side we have to say that knnn is quite
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    easy to
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    implement its Simplicity and its
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    accuracy make it one of the first
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    classifiers that a new data scientist
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    will learn it also has only a few hyper
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    parameters which is a big advantage as
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    well KNN only requires a k value and a
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    distance metric which is a lot less than
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    other machine learning algorithms also
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    in the plus category we can say that
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    it's very
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    adaptable as well meaning as new
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    training samples are added the algorithm
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    adjusts to account for any new data
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    since all training data is stored into
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    memory that sounds good but there's also
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    a drawback here
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    and that is but because of that it
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    doesn't scale very
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    well as a data set grows the algorithm
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    becomes less efficient due to increased
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    computational complexity comprising
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    compromising the overall model
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    performance and this this inability to
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    scale it comes from KNN being what's
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    called a lazy algorithm meaning it
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    stores all training data and defers the
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    computation to the time of
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    classification that results in higher
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    memory usage and slower processing
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    compared to other classifiers now KNN
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    also tends to fall victim to something
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    called The Curse of
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    dimensionality which means it doesn't
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    perform well with high dimensional data
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    inputs so in our sweetness to
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    crunchiness example this is a 2d space
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    it's relatively easy to find the nearest
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    neighbors and classify new fruits
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    accurately however if we keep adding
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    more features like color and size and
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    weight and so on the data points become
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    sparse in the high dimensional space the
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    distances between the points starts to
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    become similar making it difficult for
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    K&N to find meaningful neighbors and it
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    can also lead to something called the
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    peaking phenomenon where after reaching
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    an optimal number of features adding
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    more features just increases noise and
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    increases classification errors
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    especially when the sample size is small
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    feature selection and dimensionality
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    reduction techniques can help minimize
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    the curse of dimensionality but if not
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    done carefully they can make KNN prone
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    to another downside and that is the
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    downside of over
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    fitting lower values of K can overfit
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    the data whereas higher values of K tend
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    to smooth out the prediction values
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    since it's averaging the values over a
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    greater area or neighborhood so because
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    of all this the KNN algorithm is
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    commonly used for simple
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    recommendation systems so for example
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    the algorithm can be applied in the
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    areas of data
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    preprocessing that's pretty common use
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    case for knnn and that's because the KNN
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    algorithm is helpful for data sets with
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    missing values since it can estimate for
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    those values using a process known as
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    missing data
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    imputation now another use case is in
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    Final
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    in in the KNN algorithm it's often used
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    in stock market forecasting currency
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    exchange rates trading Futures and
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    moneya
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    laundering analysis money laundering
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    analysis and also we have to consider
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    the use
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    case for
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    healthcare it's been used to make
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    predictions on the risk of heart attacks
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    and prostate cancer by calculating the
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    most likely Gene Expressions so that's
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    KNN a simple but imperfect
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    classification and regression classifier
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    in the right context it's
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    straightforward approach is as
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    delightful as biting into a perfectly
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    classified
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    Apple if you like this video and want to
  • 00:07:47
    see more like it please like And
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    subscribe if you have any questions or
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    want to share your thoughts about this
  • 00:07:54
    topic please leave a comment below
Tags
  • KNN
  • machine learning
  • classification
  • regression
  • data science
  • distance metric
  • missing data imputation
  • healthcare
  • recommendation systems
  • dimensionality reduction