Unsupervised Machine Learning

Since unlabeled data is used in unsupervised learning, there are no predetermined results. The program independently identifies linkages, groups, and hidden patterns in the data. Data visualization, dimensionality reduction, and clustering are its primary applications. It is frequently used in fraud detection, recommendation systems, and marketing, where patterns are more important than labels.

Machine learning algorithms, also known as self-learning or algorithms, are trained on unlabeled data sets—that is, input data that has not been classified—in unsupervised learning. The appropriate algorithms are selected for training based on the tasks, or machine learning problems like associations, clustering, etc., and the data sets

The algorithms learn and deduce their own rules throughout the training phase based on the similarities, patterns, and contrasts of the data points. The algorithms do not require pre-training or labels (goal values) in order to learn. A machine learning model is the result of this algorithm's training process using data sets. The model is an unsupervised machine learning model since the data sets are unlabeled (no target values, no human supervision). The model is now prepared to carry out unsupervised learning tasks including dimensionality reduction, grouping, and association. Complex tasks, such as clustering huge datasets, are well suited for unsupervised learning models.

Outliners in Machine Learning

Outliers in machine learning are those data points that very much differ from the dataset's overall distribution. They could be the result of uncommon occurrences, natural variance, or mistakes in data gathering. Outlier detection is an important preprocessing step because, although they can occasionally provide valuable insights, such as in fraud detection, they frequently have a detrimental impact on model accuracy and skew findings..

Machine Learning Examples: Detecting Outliers in Data

Example 1: House Price Prediction (Regression)

Example 2: Employee Salary Prediction

Example 3: Healthcare - Diabetes Prediction

Example 4: Transportation Cost Prediction

Example 5: Manufacturing Machine Data

Example 6: Credit Card Fraud Detection

Example 7: Student Exam Score Prediction

Summary: Normal Data vs Outliers

Bias and variance

Bias
When a machine learning model is overly simplistic and fails to extract sufficient information from the data, bias occurs. It is similar to thinking that all birds can only be small and fly; as a result, the model becomes biased in its predictions and is unable to identify large birds like penguins or ostriches. Although these presumptions facilitate training, they may hinder the model's ability to fully capture the intricacies of the data. Underfitting, where the model performs badly on both training and testing data because it does not learn enough from the data, is usually caused by high bias.
Variance
A statistical measure of how far a set of data deviates from its mean is called variance. A large variance implies that the data is widely spread, whereas a low variance shows that the data points are closely packed around the mean. It is computed by averaging each data point's squared deviations from the mean. A high-variance model has poor generalization on new data because it learns both the patterns and the noise in the training set. Overfitting, in which the model performs well on training data but badly on testing data, is usually caused by high variance.

Noise in Machine Learning

Noise in machine learning refers to random or irrelevant data that can lead to unexpected circumstances that deviate from our expectations. Inaccurate measurements, erroneous data collecting, or irrelevant information are the causes. Noise can obscure links and patterns in data, much as background noise can obscure speech. For accurate modeling and forecasting, handling noise is crucial. Strong algorithms, data purification, and feature selection are some of the techniques that mitigate its consequences. Ultimately, noise reduction increases the effectiveness of machine learning models.

Overfitting and Underfitting Machine Learning

Overfitting
When a model learns too much from the training data—including irrelevant characteristics like noise or outliers—it is said to be overfitting. Consider fitting a complex curve to a group of points, for instance. Although the curve will pass through each point, it will not accurately depict the pattern. Because of this, the model performs exceptionally well on training data but poorly on fresh data. Overfitting models are comparable to pupils who remember solutions rather than comprehending the subject. They perform well on practice exams (training) but find it difficult to pass actual exams.
Causes of Underfitting
1. Excessive Model Complexity: Training data, including noise, can be more easily memorized by models with an excessive number of parameters or layers.
2. Overtraining (Too Many Epochs): If a model is trained for an extended period of time, it may eventually begin to learn the noise in the data, which may deteriorate its performance on new data.
3. Noisy or Irrelevant Features: The model may focus on the incorrect signals if the data contains a lot of irrelevant information.

Underfitting
The opposite of overfitting is underfitting. It occurs when a model is too basic to adequately represent the data. Consider, for instance, fitting points that genuinely follow a curve with a straight line. The majority of the pattern is missed by the line. In this instance, the model performs poorly on both training and testing data. Underfitting models are similar to understudied students. They perform poorly on both practice and actual exams. It should be noted that the underfitting model has low variance and high bias.
Causes of Underfitting
1. The model may not be able to capture the complexity of the data because it is too simplistic.
2. The underlying causes impacting the target variable are not adequately represented by the input characteristics used to train the model.
3. The training dataset's size is insufficient.