Sklearn ml. You can import the datasets and play around with them.

The library is built using many libraries you may already be familiar with, such as NumPy and SciPy. get_model_dependencies to retrieve and download the model dependencies. 9. The advantages of support vector machines are: Effective in high dimensional spaces. 13. Image feature extraction #. To use the pipeline function of scikit-learn we have to import the Pipeline module. Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like. For rebuilding an image from all its patches, use reconstruct_from_patches_2d. Feature selection #. Cross-validation: evaluating estimator performance #. Computing cross-validated metrics. Dask-ML provides scalable machine learning in Python using Dask alongside popular machine learning libraries like Scikit-Learn, XGBoost, and others. About the data from the original website: Sep 13, 2023 · In conclusion, the scikit-learn library provides us with three important methods, namely fit (), transform (), and fit_transform () Sklearn, that are used widely in machine learning. The breast cancer dataset is a classic and very easy binary classification dataset. 18. The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection method which computes the local density deviation of a given data point with respect to its neighbors. For string or object data types, fill_value must be a string. Multi-layer Perceptron #. To prevent such non-finite numbers to pollute higher-level experiments such as a Scikit-learn, also known as sklearn, is an open-source, robust Python machine learning library. Model selection and evaluation #. 0, iterated_power='auto', n_oversamples=10, power_iteration_normalizer='auto', random_state=None) [source] #. AutoML often involves the use of sophisticated optimization algorithms, such as Bayesian Optimization, to efficiently navigate the space of possible . In the process, we introduce how to perform periodic feature engineering using the sklearn class sklearn. The mlflow. In the samples training folder, find a completed and expanded notebook by navigating to this directory: v2 > sdk > jobs > single-step > scikit-learn > train-hyperparameter-tune-deploy-with-sklearn. 0, force_alpha=True, binarize=0. There are many ways to make a pipeline but I will show one of the easiest and smart versions of them in this blog. We will be using scikit-learn (python) libraries for our example. The wine dataset is a classic and very easy multi-class classification dataset. ndarray. The purpose of this guide is to illustrate some of the main features that scikit-learn provides. class sklearn. Step 2: Loading the data set in jupyter. Multi-layer Perceptron (MLP) is a supervised learning algorithm that learns a function f: R m → R o by training on a dataset, where m is the number of dimensions for input and o is the number of dimensions for output. there might be multiple versions of the ‘iris’ dataset). This notebook introduces different strategies to leverage time-related features for a bike sharing demand regression task that is highly dependent on business cycles (days, weeks, months) and yearly season cycles. You can import these notebooks and run them in your Databricks workspace. Built around the scikit-learn machine learning library, auto-sklearn automatically searches for the right learning algorithm for a new machine learning dataset and optimizes its hyperparameters. accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None) [source] #. This data sets consists of 3 different types of irises’ (Setosa, Versicolour, and Virginica) petal and sepal length, stored in a 150x4 numpy. Model persistence #. pipeline import Pipeline fetch_openml. feature selection, if i compre with weka, it provides various feature selection methods and result gives you a list of selected First, we specify our features X and target variable Y and split the dataset into training and test sets. nan option was added. #. The statistic is also known as the phi coefficient. #Encode categorical variable into numerical ones from sklearn. It tries to find a function that best predicts the continuous output value for a given input value. 20, sklearn deprecates fetch_mldata function and adds fetch_openml instead. Jan 1, 2010 · Linear Models- Ordinary Least Squares, Ridge regression and classification, Lasso, Multi-task Lasso, Elastic-Net, Multi-task Elastic-Net, Least Angle Regression, LARS Lasso, Orthogonal Matching Pur Mar 24, 2019 · from sklearn. The extract_patches_2d function extracts patches from an image stored as a two-dimensional array, or three-dimensional with color information along the third axis. The slope of a straight line is the projected output of the supervised machine learning process known as linear regression. Manifold learning #. Parameters: Xarray-like of shape (n_samples, n_features) The input samples. OPTICS is particularly adept at uncovering hidden patterns and structures within datasets, making The MCC is in essence a correlation coefficient value between -1 and +1. As a test case, we will classify animal photos, but of course the methods described can be applied to all kinds of machine learning problems. This course is a practical and hands-on introduction to Machine Learning with Python and Scikit-Learn for beginners with basic knowledge of Python and statis 1. Dataset transformations — scikit-learn 1. Both the number of properties and the number of classes per property is greater than 2. 0, 1. And we will learn how to make functions that are able to predict the outcome based on what we have learned. Multiclass-multioutput classification (also known as multitask classification) is a classification task which labels each sample with a set of non-binary properties. The precision is intuitively the ability of the SelectKBest #. feature_selection. When strategy == “constant”, fill_value is used to replace all occurrences of missing_values. naive_bayes. The solver for weight optimization. metrics. Apr 10, 2018 · In this tutorial, we will set up a machine learning pipeline in scikit-learn to preprocess data and train a model. decomposition. A tree can be seen as a piecewise constant approximation. 6. The important dictionary keys to consider are the classification label names (target_names), the actual labels (target), the attribute/feature names (feature_names), and the attributes (data). This classifier serves as a simple baseline to compare against other more complex classifiers. Fit label encoder. The library enables practitioners to rapidly implement a vast range of supervised and unsupervised machine learning algorithms through a In this tutorial we will go back to mathematics and study statistics, and how to calculate important numbers based on data sets. Nov 25, 2019 · ML | Voting Classifier using Sklearn. Given a set of features X = x 1, x 2,, x m and a target y, it can learn a non-linear May 29, 2020 · The scikit-learn Python machine learning library provides this capability via the n_jobs argument on key machine learning tasks, such as model training, model evaluation, and hyperparameter tuning. log_loss (y_true, y_pred, *, normalize = True, sample_weight = None, labels = None) [source] # Log loss, aka logistic loss or cross-entropy loss. 1 documentation. You can use the prepopulated code in the sample training folder to complete this tutorial. May 22, 2024 · Sklearn's OPTICS, an acronym for Ordering Points To Identify the Clustering Structure, stands as a powerful tool in the realm of machine learning and data analysis. Based on your use-case, there are a few different ways to persist a scikit-learn model, and here we help you decide which one suits you In Databricks Runtime 11. 24 with Python 3. A linear kernel is a simple dot product between two input vectors, while a non-linear Auto-sklearn provides out-of-the-box supervised machine learning. 0, np. 5. Datasets are uniquely identified by either an integer ID or by a combination of name and version (i. Naive Bayes #. Classes. The factor multiplying the hypercube size. In scikit-learn i m strugling badely at one point i. Note that the default setting flip_y > 0 might lead to less than n_classes in y in some cases. Text summary of the precision, recall, F1 score for each class. This is the loss function used in (multinomial) logistic regression and extensions of it such as neural networks, defined as the negative log-likelihood of a logistic model that returns y_pred probabilities for its training Gallery examples: Features in Histogram Gradient Boosting Trees Lagged features for time series forecasting Nov 22, 2023 · But why sklearn ? Among the ML libraries, scikit-learn is the de facto simplest and easiest framework to learn ML. Larger values introduce noise in the labels and make the classification task harder. PowerTransformer(method='yeo-johnson', *, standardize=True, copy=True) [source] #. If set to “warn”, this acts as 0, but warnings are also raised. In the particular case when y_true is constant, the explained variance score is not finite: it is either NaN (perfect predictions) or -Inf (imperfect predictions). It assumes a very basic working knowledge of machine learning practices (model fitting, predicting, cross-validation, etc. You can review the preprocess API in scikit-learn here. It also implements “score_samples”, “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used. This is useful for modeling issues related to Aug 16, 2020 · Presently i am dealing with scikit-learn and has some previus experince with WEKA 6, which is a best open source GUI based tool for ML as best of my undestaing. zero_division{“warn”, 0. We use scikit-learn's train_test_split () method to split the dataset into 70% training and 30% test data. Load and return the wine dataset (classification). , Manifold learning- Introduction, Isomap, Locally Linear Embedding, Modified Locally Linear Embedding, Hessian Eige scikit-learn is one of the most popular Python libraries for single-node machine learning and is included in Databricks Runtime and Databricks Runtime ML. The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed. g. 0 ML and above, for pyfunc flavor models, you can call mlflow. For each row x of X and class y, the joint log probability is given by log P(x, y) = log P(y) + log P(x|y), where log P(y) is the class prior probability and log P(x|y) is the class-conditional probability. ). 2. You can also find the best hyperparameter, data preparation method, and machine learning model with grid search and the passthrough keyword. Nearest Neighbors #. Cross-validation: evaluating estimator performance. Here, we combine 3 learners (linear and non-linear) and use a ridge Gallery examples: Lagged features for time series forecasting load_wine. Jan 30, 2023 · Support vector regression (SVR) is a type of support vector machine (SVM) that is used for regression tasks. dummy. fill_valuestr or numerical value, default=None. Download MNIST dataset with the following code: from sklearn. log_loss# sklearn. When your data is comprised of attributes with varying scales, many machine learning algorithms can benefit from rescaling the attributes to all have the same scale. Fitted label encoder. You can find my code in this GitHub. This example shows how to use LOF for outlier detection which is Scikit-Learn ML Algorithms. See Databricks Runtime release notes for the scikit-learn library version included with your cluster’s runtime. 9. 3. The sklearn. Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable. This implementation works with data represented as dense or sparse arrays of floating point values for the features. We will also learn how to use various Python modules to get the answers we need. Sep 11, 2020 · AutoML With Auto-Sklearn. This package also features helpers to fetch larger datasets commonly used by the machine learning community to benchmark algorithms on data that Added in version 0. iris import Iris app = FastAPI(title="Iris ML API", description="API for iris dataset ml class sklearn. Sets the value to return when there is a zero division. Target values. Added in version 0. e. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. Support Vector Machines #. load_breast_cancer (*, return_X_y = False, as_frame = False) [source] # Load and return the breast cancer wisconsin dataset (classification). y ^ ( w, x) = w 0 + w 1 x 1 + + w p x p. Supervised neighbors-based learning comes in two flavors: classification for data 1. The parameters of the estimator used to apply these methods are optimized by cross-validated class sklearn. In multilabel classification, this function computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true. ml. Jaccard similarity coefficient score. 3 Recognizing hand-written digits A demo of K-Means clustering on the handwritten digits data Feature agglomeration Various Agglomerative Clu Jul 23, 2017 · Also, little bit of python and ML basics including text classification is required. Successive Halving Iterations. 11. You can import the datasets and play around with them. The implementation is a wrapper around SGDClassifier by fixing the loss and learning_rate parameters as: SGDClassifier(loss="perceptron", learning_rate="constant") Other available parameters are described below and are forwarded to SGDClassifier. , word counts for text classification). Bayes’ theorem states the following relationship, given class variable y and dependent feature Mar 26, 2024 · Select the notebook tab in the Azure Machine Learning studio. Dataset transformations #. 1, on Linux. One of its best features is the ease with which you can create custom estimators, allowing you to meet specific needs. However, in practice, fractional counts such as tf-idf may also work. Ordinary least squares Linear Regression. 8. X = df. nan}, default=”warn”. Automated Machine Learning, or AutoML for short, is a process of discovering the best-performing pipeline of data transforms, model, and model configuration for a dataset. It’s built upon some of the technology you might already be familiar with, like NumPy Dec 11, 2019 · The scikit-learn documentation has some information on how to use various different preprocessing methods. BernoulliNB(*, alpha=1. Cross-validation: evaluating estimator performance — scikit-learn 1. Supervised learning. Scikit-learn has a fantastic performance running only on the CPU due to smart NumPy vectorized executions, which are friendly to CPU cache lines, and highly efficient use of the CPU available cores. Jan 5, 2022 · Scikit-Learn is a free machine learning library for Python. ‘logistic’, the logistic sigmoid function, returns f (x) = 1 / (1 + exp (-x)). Preprocessing data #. Returns: reportstr or dict. Dataset loading utilities #. For instance, mnist['target'] is an array of string category labels (not floats as The features are encoded using a one-hot (aka ‘one-of-K’ or ‘dummy’) encoding scheme. SVR can use both linear and non-linear kernels. LinearRegression(*, fit_intercept=True, copy_X=True, n_jobs=None, positive=False) [source] #. 4. preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream estimators. Scikit-learn is a library in Python that provides many unsupervised and supervised learning algorithms. Added in version 1. model_selection import train_test_split. classifier as clf from fastapi import FastAPI, Body from joblib import load from models. 1. The formula for the F1 score is: F1 = 2 ∗ TP 2 ∗ TP + FP + FN. This configuration argument allows you to specify the number of cores to use for the task. Here is a list of several typical Scikit-learn algorithms and techniques, given in decreasing order of complexity: Linear Regression Algorithm Example. Read more in the User Guide. It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels. Jul 21, 2020 · 1. mlflow. Like MultinomialNB, this classifier is suitable for discrete data. This tutorial is the fourth installment of the series of articles on the RAPIDS ecosystem. Key changes include: Computational tools: The exploitation of GPUs, distributed programming frameworks like Scala/Spark, etc. Aug 15, 2022 · Scikit-Learn: ML Model Evaluation Metrics (Classification, Regression, and Clustering Metrics)¶ Machine Learning and Artificial Intelligence are the most trending topics of 21st century. Mar 21, 2024 · Scikit-learn is a powerful machine learning library in Python that offers a wide range of tools for data analysis and modeling. 3. SelectKBest. Key concepts and features include: Algorithmic decision-making methods, including: Classification: identifying and categorizing data based on patterns. After that, you have to import SVM which stands for Support Vector Machine class sklearn. 0, fit_prior=True, class_prior=None) [source] #. This is the main flavor that can be loaded back into scikit-learn. Ensembles: Gradient boosting, random forests, bagging, voting, stacking#. In this article, we will walk through the process of building a custom estimator in Scikit-learn, compl User Guide. Dec 4, 2017 · Scikit learn comes with sample datasets, such as iris and digits. Python3. This module exports scikit-learn models with the following flavors: Python (native) pickle format. The multinomial Naive Bayes classifier is suitable for classification with discrete features (e. Step 1: Importing the required libraries. Choosing min_resources and the number of candidates#. LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted The fraction of samples whose class is assigned randomly. Unsupervised learning. from sklearn. 17. Statement of purpose: Scikit-learn in 2018 #. For this tutorial we used scikit-learn version 0. 0, lower values are worse. Getting Started#. Sep 8, 2022 · You can implement the Scikit-learn pipeline and ColumnTransformer from the data cleaning to the data modeling steps to make your code neater. Stacking provide an alternative by combining the outputs of several learners, without the need to choose a model specifically. For best results using the default learning rate schedule, the data should have zero mean and unit variance. Metrics and scoring: quantifying the quality of predictions #. Naive Bayes classifier for multivariate Bernoulli models. 10. Gaussian mixture models- Gaussian Mixture, Variational Bayesian Gaussian Mixture. Support Vector Machines — scikit-learn 1. Density Estimation: Histograms. Linear dimensionality reduction using Singular Value Decomposition of the data to project it to Dask-ML¶. linear_model. The relative contribution of precision and recall to the F1 score are equal. High-level Python libraries for experimentation, processing and data Linear perceptron classifier. Explained variance regression score function. 3: np. Activation function for the hidden layer. datasets. The data set will be using for this example is the famous “20 Newsgoup” data set. You can try Dask-ML on a small cloud instance by clicking the following button: Jul 29, 2020 · Refresh the page, check Medium ’s site status, or find something interesting to read. Ensemble methods combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator. Principal component analysis (PCA). The Iris Dataset. The following are a set of methods intended for regression in which the target value is expected to be a linear combination of the features. Across the module, we designate the vector w SGD allows minibatch (online/out-of-core) learning via the partial_fit method. selfreturns an instance of self. [source: Wikipedia] Binary and multiclass labels are supported. Model selection and evaluation. PCA(n_components=None, *, copy=True, whiten=False, svd_solver='auto', tol=0. Many ML Models can help automate tasks that were otherwise needed manual actions. Samples per class. preprocessing import LabelEncoder list_var = [‘country’, ‘city’] May 22, 2024 · This article aims to implement the L2 and L1 regularization for Linear regression using the Ridge and Lasso modules of the Sklearn library of Python. preprocessing. 2. Model persistence — scikit-learn 1. Best possible score is 1. A single estimator thus handles several joint classification tasks. Time-related feature engineering #. Jul 5, 2020 · import models. 0 GridSearchCV implements a “fit” and a “score” method. sklearn module provides an API for logging and loading scikit-learn models. Metrics and scoring: quantifying the quality of predictions — scikit-learn 1. 1. Everyone is trying different types of ML models to solve their tasks. feature_selection module can be used for feature selection/dimensionality reduction on sample sets, either to improve estimators’ accuracy scores or to boost their performance on very high-dimensional datasets. Where TP is the number of true positives, FN is the scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. Decision Trees #. By default, the encoder derives the categories based on the unique values in each feature. Transformer that performs Sequential Feature Selection. The specific behavior of the baseline is selected with the strategy parameter. The rows being the samples and the columns being: Sepal Length, Sepal Width, Petal Length and Petal Width. scikit-learn: machine learning in Python — scikit-learn 1. datasets package embeds some small toy datasets as introduced in the Getting Started section. The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The Jaccard index [1], or Jaccard similarity coefficient, defined as the size of the intersection divided by the size of the union of two label sets, is used to compare set of predicted labels for a sample to the corresponding set of labels in y_true. Fetch dataset from openml by name or dataset id. Compute the precision. 6. After training a scikit-learn model, it is desirable to have a way to persist the model for future use without having to retrain. Support vector machines (SVMs) are a set of supervised learning methods used for classification , regression and outliers detection. Classification: Classification involves teaching a computer Examples. Rescale Data. Introduction #. In general, many learning algorithms such as linear models benefit from standardization of the data set (see 1. SelectKBest(score_func=<function f_classif>, *, k=10) [source] #. scikit-learn provides a library of transformers, which may clean (see Preprocessing data ), reduce (see Unsupervised dimensionality reduction ), expand (see Kernel Approximation) or generate (see Feature extraction ) feature representations. Fit label encoder and return encoded labels. neighbors provides functionality for unsupervised and supervised neighbors-based learning methods. import pandas as pd. precision_score(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn') [source] #. sklearn. It simply aggregates the findings of each classifier passed into Voting Classifier and predicts the output class LabelEncoder can be used to normalize labels. There are 3 different APIs for evaluating the quality of a model’s predictions: Estimator score method: Estimators have a score method providing a default evaluation criterion Dataset loading utilities — scikit-learn 1. Linear Models #. Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues Jun 7, 2016 · I’m using sklearn to do that, but I don’t know if we can (as for Spark), integrate this transformation with the ML model into the serialized file (Pickle or Joblib). The fit () method helps in fitting the data into a model, transform () method helps in transforming the data into a form that is more suitable for the model. If None, fill_value will be 0 when imputing numerical data and Mar 22, 2021 · Scikit-learn Tutorial – Beginner’s Guide to GPU Accelerated ML Pipelines. datasets import fetch_openml mnist = fetch_openml('mnist_784') There are some changes to the format though. Comparison between grid search and successive halving. The classes in the sklearn. pyfunc. Kernel Density Estimation. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB is As of version 0. It is a part of the Scikit-learn library, a popular machine-learning library in Python. DummyClassifier(*, strategy='prior', random_state=None, constant=None) [source] #. User Guide. class_sepfloat, default=1. Accuracy classification score. Apply a power transform featurewise to make data more Gaussian-like. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high. This function returns a path to the dependencies file which you can then install by using %pip install <file-path> . Manifold learning is an approach to non-linear dimensionality reduction. This creates a binary column for each category and returns a sparse matrix or dense array (depending on the sparse_output parameter). Parameters: score_funccallable, default=f_classif. Model selection and evaluation — scikit-learn 1. A coefficient of +1 represents a perfect prediction, 0 an average random prediction and -1 an inverse prediction. drop(['total_count'],axis=1) Gallery examples: Release Highlights for scikit-learn 1. Apr 12, 2024 · Scikit-learn is an open-source machine learning library that provides simple and efficient tools for data analysis and modeling. Unsupervised nearest neighbors is the foundation of many other learning methods, notably manifold learning and spectral clustering. load_wine(*, return_X_y=False, as_frame=False) [source] #. In mathematical notation, if y ^ is the predicted value. Eleven years after the inception of Scikit-learn, much has changed in the world of machine learning. Beside factor, the two main parameters that influence the behaviour of a successive halving search are the min_resources parameter, and the number of candidates (or parameter combinations) that are evaluated. Patch extraction #. Cross validation iterators. Thus, it frees the machine learning practitioner from these tedious tasks and allows The F1 score can be interpreted as a harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. Select features according to the k highest scores. ‘tanh’, the hyperbolic tan function, returns f (x) = tanh (x). The multinomial distribution normally requires integer feature counts. Aug 10, 2020 · In this article, I write about how to create pipelines in scikit-learn to show the magical world of them. It supports both supervised and unsupervised machine learning, providing diverse algorithms for classification, regression, clustering, and dimensionality reduction. . This Sequential Feature Selector adds (forward selection) or removes (backward selection) features to form a Mar 9, 2023 · This article will present a code implementation for ML Pipelines using two of the main libraries available: Apache Spark’s MLLib and Scikit-learn. It is built on NumPy, SciPy, and Matplotlib, making it a powerful tool for tasks like classification, regression, clustering, and dimensionality reduction. A Voting Classifier is a machine learning model that trains on an ensemble of numerous models and predicts an output (class) based on their highest probability of chosen class as the output. The performance of stacking is usually close to the best model and sometimes it can outperform the prediction performance of each individual model. Dataset – House prices dataset. The below plot uses the first two features. datasets import load_breast_cancer # Load dataset data = load_breast_cancer The data variable represents a Python object that works like a dictionary. The series explores and discusses various aspects of RAPIDS that allow its users solve ETL (Extract, Transform, Load) problems, build ML (Machine Learning) and DL (Deep Learning accuracy_score. The ith element represents the number of neurons in the ith hidden layer. Removing features with low variance Mar 8, 2021 · Scikit-learn is an open source data analysis library, and the gold standard for Machine Learning (ML) in the Python ecosystem. Leverage domain knowledge and object-oriented programming (OOP) to build hybrid rule-based machine learning (ML) models on top of scikit-learn. It considers as outliers the samples that have a substantially lower density than their neighbors. The default is None, which will use a single core. See the About us page for a list of core contributors. DummyClassifier makes predictions that ignore the input features. Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat the labels of the class sklearn. 0. It is based on the scientific stack (mostly NumPy), focuses on traditional yet powerful algorithms like linear regression/support vector machines/dimensionality reductions, and provides lots of tools to build around those algorithms (like model evaluation and selection 3. Please give either name or data_id (not both). SequentialFeatureSelector(estimator, *, n_features_to_select='auto', tol=None, direction='forward', scoring=None, cv=5, n_jobs=None) [source] #. Manifold learning — scikit-learn 1. It was created to help simplify the process of implementing machine learning and statistical models in Python. Regression: predicting or projecting data values based on the 1. Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. Restricted Boltzmann machines. 5: strategy=callable for custom value imputation. Neural network models (unsupervised) 2. 7. xy kz wf ut jj zm ow oe wd tl