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Anomaly Detection Example with Local Outlier Factor in Python

The Local Outlier Factor is an algorithm to detect anomalies in observation data. Measuring the local density score of each sample and weighting their scores are the main concept of the algorithm. By comparing the score of the sample to its neighbors, the algorithm defines the lower density elements as anomalies in data.

"The local outlier factor is based on a concept of a local density, where locality is given by ${\displaystyle k}$ nearest neighbors, whose distance is used to estimate the density. By comparing the local density of an object to the local densities of its neighbors, one can identify regions of similar density, and points that have a substantially lower density than their neighbors. These are considered to be outliers."

In this tutorial, we'll learn how to detect anomaly in a dataset by using the Local Outlier Factor method in Python. The Scikit-learn API provides the LocalOutlierFactor class for this algorithm and we'll use it in this tutorial. The tutorial covers:
1. Preparing the dataset
2. Defining the model and prediction
3. Anomaly detection with scores
4. Source code listing

Anomaly Detection with Isolation Forest in Python

Anomaly or outlier is an element with the properties that differ from the majority of the observation data. Anomalies may define the errors, extremes, or abnormal cases in observation data. There are several methods to detect anomalies in a dataset. Isolation Forest is one of the anomaly detection methods.

Isolation forest is a learning algorithm for anomaly detection by isolating the instances in the dataset. The algorithm creates isolation trees (iTrees), holding the path length characteristics of the instance of the dataset and Isolation Forest (iForest) applies no distance or density measures to detect anomalies. To learn more about the algorithm, please refer to the links listed in the reference section.

In this tutorial, we'll learn how to detect anomaly in the dataset by using the Isolation Forest method in Python. The Scikit-learn API provides the IsolationForest class for this algorithm and we'll use it in this tutorial. The tutorial covers:
1. Preparing the dataset
2. Defining the model and prediction
3. Anomaly detection with scores
4. Source code listing

Convolutional Autoencoder Example with Keras in Python

Autoencoder is a neural network model that learns from the data to imitate the output based on input data. It can only represent a data-specific and lossy version of the trained data. Thus the autoencoder is a compression and reconstructing method with a neural network.
When it comes to image data, principally we use the convolutional neural networks in building the deep learning model. In the previous post, we learned how to build simple autoencoders with dense layers. In this tutorial, we'll learn how to build autoencoders by applying the convolutional neural networks with Keras in Python. The tutorial covers:
1. Preparing the data
2. Defining the convolutional autoencoder
3. Generating the images
4. Source code listing

```from keras.layers import Conv2D
from keras.layers import Input```
```from keras.layers import MaxPooling2D, UpSampling2D
from keras.models import Model
from numpy import reshape
import matplotlib.pyplot as plt```

Simple Autoencoder Example with Keras in Python

Autoencoder is a neural network model that learns from the data to imitate the output based on the input data. It can only represent a data-specific and a lossy version of the trained data. Autoencoder is also a kind of compression and reconstructing method with a neural network. In this tutorial, we'll learn how to build a simple autoencoder with Keras in Python. The tutorial covers:
1. Preparing the data
2. Defining the autoencoder model
3. Restoring the image
4. Source code listing

```from keras.layers import Dense
from keras.layers import Input, LeakyReLU
from keras.models import Model
from numpy import reshape
import matplotlib.pyplot as plt```

Convolutional Autoencoder Example with Keras in R

We can apply the convolutional neural networks to build the autoencoders. In this tutorial, we'll briefly learn how to build convolutional neural networks with Keras in R. Autoencoder learns to compress the given data and reconstructs the output according to the data trained on. It can only represent a data specific and a lossy version of the trained data. Convolutional networks perform well with image data. To train the autoencoder, we'll use the MNIST handwritten digits dataset. The tutorial covers:
1. Preparing the data
2. Defining the model
3. Generating from test data
4. Source code listing
We'll start by loading the required Keras package in R. Note that for this tutorial we need the R interface of Keras API and RStudio.

`library(keras)`

Multi-output Classification Example with MultiOutputClassifier in Python

Scikit-learn API provides a MulitOutputClassifier class that helps to classify multi-output data. In this tutorial, we'll learn how to classify multi-output (multi-label) data with this method in Python. Multi-output data contains more than one y label data for a given X input data. The tutorial covers:
1. Preparing the data
2. Defining the model
3. Predicting and accuracy check
4. Source code listing
```from sklearn.model_selection import train_test_split