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Top 10 Examples of "jupyter in functional component" in Python

Dive into secure and efficient coding practices with our curated list of the top 10 examples showcasing 'jupyter' in functional components in Python. Our advanced machine learning engine meticulously scans each line of code, cross-referencing millions of open source libraries to ensure your implementation is not just functional, but also robust and secure. Elevate your React applications to new heights by mastering the art of handling side effects, API calls, and asynchronous operations with confidence and precision.

from enum import Enum


class Estimator(Enum):
    SVC = "SVC"
    KNeighborsClassifier = "KNeighborsClassifier"
    RandomForestClassifier = "RandomForestClassifier"
    SGDClassifier = "SGDClassifier"
    SGDRegressor = "SGDRegressor"
    SVR = "SVR"
    MiniBatchKMeans = "MiniBatchKMeans"
    KMeans = "KMeans"


content = {}
content[Estimator.SVC.value] = {
    "import": "from sklearn.svm import SVC",
    "doc_link": "https://scikit-learn.org/stable\
    /modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC",
    "evaluation": "classification",
}
content[Estimator.KNeighborsClassifier.value] = {
    "import": "from sklearn.neighbors import KNeighborsClassifier",
    "doc_link": "https://scikit-learn.org/stable/modules/neighbors.html",
    "evaluation": "classification",
}
content[Estimator.RandomForestClassifier.value] = {
    "import": "from sklearn.ensemble import RandomForestClassifier",
    "doc_link": "https://scikit-learn.org/stable\
    /modules/generated/sklearn.ensemble.Random\
    ForestClassifier.html#sklearn.ensemble.RandomForestClassifier",
    "evaluation": "classification",
"evaluation": "regression",
}
content[Estimator.SVR.value] = {
    "import": "from sklearn.svm import SVR",
    "doc_link": "https://scikit-learn.org/\
    stable/modules/generated/sklearn.svm.\
    SVR.html#sklearn.svm.SVR",
    "evaluation": "regression",
}
content[Estimator.MiniBatchKMeans.value] = {
    "import": "from sklearn.cluster import MiniBatchKMeans",
    "doc_link": "https://scikit-learn.org/\
    stable/modules/clustering.html#mini-batch-k-means",
    "evaluation": "classification",
}
content[Estimator.KMeans.value] = {
    "import": "from sklearn.cluster import KMeans",
    "doc_link": "https://scikit-learn.org/\
    stable/modules/clustering.html#k-means",
    "evaluation": "clustering",
}


metrics = {
    "regression": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Regression Evaluation Metrics\n",
                "\n",
                "\n",
content[Estimator.RandomForestClassifier.value] = {
    "import": "from sklearn.ensemble import RandomForestClassifier",
    "doc_link": "https://scikit-learn.org/stable\
    /modules/generated/sklearn.ensemble.Random\
    ForestClassifier.html#sklearn.ensemble.RandomForestClassifier",
    "evaluation": "classification",
}
content[Estimator.SGDClassifier.value] = {
    "import": "from sklearn.linear_model import SGDClassifier",
    "doc_link": "https://scikit-learn.org/\
    stable/modules/generated/sklearn.linear\
    _model.SGDClassifier.html#sklearn.\
    linear_model.SGDClassifier",
    "evaluation": "classification",
}
content[Estimator.SGDRegressor.value] = {
    "import": "from sklearn.linear_model import SGDRegressor",
    "doc_link": "https://scikit-learn.org\
    /stable/modules/generated/sklearn.linear\
    _model.SGDRegressor.html#sklearn.linear_model.SGDRegressor",
    "evaluation": "regression",
}
content[Estimator.SVR.value] = {
    "import": "from sklearn.svm import SVR",
    "doc_link": "https://scikit-learn.org/\
    stable/modules/generated/sklearn.svm.\
    SVR.html#sklearn.svm.SVR",
    "evaluation": "regression",
}
content[Estimator.MiniBatchKMeans.value] = {
    "import": "from sklearn.cluster import MiniBatchKMeans",
    "doc_link": "https://scikit-learn.org/\
RandomForestClassifier = "RandomForestClassifier"
    SGDClassifier = "SGDClassifier"
    SGDRegressor = "SGDRegressor"
    SVR = "SVR"
    MiniBatchKMeans = "MiniBatchKMeans"
    KMeans = "KMeans"


content = {}
content[Estimator.SVC.value] = {
    "import": "from sklearn.svm import SVC",
    "doc_link": "https://scikit-learn.org/stable\
    /modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC",
    "evaluation": "classification",
}
content[Estimator.KNeighborsClassifier.value] = {
    "import": "from sklearn.neighbors import KNeighborsClassifier",
    "doc_link": "https://scikit-learn.org/stable/modules/neighbors.html",
    "evaluation": "classification",
}
content[Estimator.RandomForestClassifier.value] = {
    "import": "from sklearn.ensemble import RandomForestClassifier",
    "doc_link": "https://scikit-learn.org/stable\
    /modules/generated/sklearn.ensemble.Random\
    ForestClassifier.html#sklearn.ensemble.RandomForestClassifier",
    "evaluation": "classification",
}
content[Estimator.SGDClassifier.value] = {
    "import": "from sklearn.linear_model import SGDClassifier",
    "doc_link": "https://scikit-learn.org/\
    stable/modules/generated/sklearn.linear\
    _model.SGDClassifier.html#sklearn.\
def grid_search(estimator):
    if estimator not in [Estimator.SVC.value, Estimator.SVR.value]:
        return []

    return [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Gridsearch\n",
                "\n",
                "Finding the right parameters (like what C or gamma values to use) \
                is a tricky task. We can adopt a trial and error approach to \
                find the best fit. Through GridSearch, we can try different \
                combinations of parameters and roll with the best option. \
                You just need to feed a dictionary with possible parameters \
                and Scikit-learn will use the one with \
                the best score on the next train fit!",
}
content[Estimator.SGDRegressor.value] = {
    "import": "from sklearn.linear_model import SGDRegressor",
    "doc_link": "https://scikit-learn.org\
    /stable/modules/generated/sklearn.linear\
    _model.SGDRegressor.html#sklearn.linear_model.SGDRegressor",
    "evaluation": "regression",
}
content[Estimator.SVR.value] = {
    "import": "from sklearn.svm import SVR",
    "doc_link": "https://scikit-learn.org/\
    stable/modules/generated/sklearn.svm.\
    SVR.html#sklearn.svm.SVR",
    "evaluation": "regression",
}
content[Estimator.MiniBatchKMeans.value] = {
    "import": "from sklearn.cluster import MiniBatchKMeans",
    "doc_link": "https://scikit-learn.org/\
    stable/modules/clustering.html#mini-batch-k-means",
    "evaluation": "classification",
}
content[Estimator.KMeans.value] = {
    "import": "from sklearn.cluster import KMeans",
    "doc_link": "https://scikit-learn.org/\
    stable/modules/clustering.html#k-means",
    "evaluation": "clustering",
}


metrics = {
    "regression": [
        {
/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC",
    "evaluation": "classification",
}
content[Estimator.KNeighborsClassifier.value] = {
    "import": "from sklearn.neighbors import KNeighborsClassifier",
    "doc_link": "https://scikit-learn.org/stable/modules/neighbors.html",
    "evaluation": "classification",
}
content[Estimator.RandomForestClassifier.value] = {
    "import": "from sklearn.ensemble import RandomForestClassifier",
    "doc_link": "https://scikit-learn.org/stable\
    /modules/generated/sklearn.ensemble.Random\
    ForestClassifier.html#sklearn.ensemble.RandomForestClassifier",
    "evaluation": "classification",
}
content[Estimator.SGDClassifier.value] = {
    "import": "from sklearn.linear_model import SGDClassifier",
    "doc_link": "https://scikit-learn.org/\
    stable/modules/generated/sklearn.linear\
    _model.SGDClassifier.html#sklearn.\
    linear_model.SGDClassifier",
    "evaluation": "classification",
}
content[Estimator.SGDRegressor.value] = {
    "import": "from sklearn.linear_model import SGDRegressor",
    "doc_link": "https://scikit-learn.org\
    /stable/modules/generated/sklearn.linear\
    _model.SGDRegressor.html#sklearn.linear_model.SGDRegressor",
    "evaluation": "regression",
}
content[Estimator.SVR.value] = {
    "import": "from sklearn.svm import SVR",
KMeans = "KMeans"


content = {}
content[Estimator.SVC.value] = {
    "import": "from sklearn.svm import SVC",
    "doc_link": "https://scikit-learn.org/stable\
    /modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC",
    "evaluation": "classification",
}
content[Estimator.KNeighborsClassifier.value] = {
    "import": "from sklearn.neighbors import KNeighborsClassifier",
    "doc_link": "https://scikit-learn.org/stable/modules/neighbors.html",
    "evaluation": "classification",
}
content[Estimator.RandomForestClassifier.value] = {
    "import": "from sklearn.ensemble import RandomForestClassifier",
    "doc_link": "https://scikit-learn.org/stable\
    /modules/generated/sklearn.ensemble.Random\
    ForestClassifier.html#sklearn.ensemble.RandomForestClassifier",
    "evaluation": "classification",
}
content[Estimator.SGDClassifier.value] = {
    "import": "from sklearn.linear_model import SGDClassifier",
    "doc_link": "https://scikit-learn.org/\
    stable/modules/generated/sklearn.linear\
    _model.SGDClassifier.html#sklearn.\
    linear_model.SGDClassifier",
    "evaluation": "classification",
}
content[Estimator.SGDRegressor.value] = {
    "import": "from sklearn.linear_model import SGDRegressor",
content[Estimator.SVC.value] = {
 "import": "from sklearn.svm import SVC",
 "doc_link": "https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC",
 "evaluation": "classification"
}
content[Estimator.KNeighborsClassifier.value] = {
 "import": "from sklearn.neighbors import KNeighborsClassifier",
 "doc_link": "https://scikit-learn.org/stable/modules/neighbors.html",
 "evaluation": "classification"
}
content[Estimator.RandomForestClassifier.value] = {
 "import": "from sklearn.ensemble import RandomForestClassifier",
 "doc_link": "https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier",
 "evaluation": "classification"
}
content[Estimator.SGDClassifier.value] = {
 "import": "from sklearn.linear_model import SGDClassifier",
 "doc_link": "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier",
 "evaluation": "classification"
}
content[Estimator.SGDRegressor.value] = {
 "import": "from sklearn.linear_model import SGDRegressor",
 "doc_link": "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor",
 "evaluation": "regression"
}
content[Estimator.SVR.value] = {
 "import": "from sklearn.svm import SVR",
 "doc_link": "https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR",
 "evaluation": "regression"
}
content[Estimator.MiniBatchKMeans.value] = {
 "import": "from sklearn.cluster import MiniBatchKMeans",
content[Estimator.SGDClassifier.value] = {
 "import": "from sklearn.linear_model import SGDClassifier",
 "doc_link": "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier",
 "evaluation": "classification"
}
content[Estimator.SGDRegressor.value] = {
 "import": "from sklearn.linear_model import SGDRegressor",
 "doc_link": "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor",
 "evaluation": "regression"
}
content[Estimator.SVR.value] = {
 "import": "from sklearn.svm import SVR",
 "doc_link": "https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html#sklearn.svm.SVR",
 "evaluation": "regression"
}
content[Estimator.MiniBatchKMeans.value] = {
 "import": "from sklearn.cluster import MiniBatchKMeans",
 "doc_link": "https://scikit-learn.org/stable/modules/clustering.html#mini-batch-k-means",
 "evaluation": "classification"
}
content[Estimator.KMeans.value] = {
 "import": "from sklearn.cluster import KMeans",
 "doc_link": "https://scikit-learn.org/stable/modules/clustering.html#k-means",
 "evaluation": "clustering"
}


metrics = {
 "regression": [
  {
    "cell_type": "markdown",
    "metadata": {},

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