Machine Learning Types, Advantages and Disadvantages

Machine learning (ML) is a field of inquiry devoted to understanding and building methods that ‘learn’, that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers, but not all machine learning is statistical learning. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain. In its application across business problems, machine learning is also referred to as predictive analytics.

Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as “since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well”. They can be nuanced, such as “X% of families have geographically separate species with color variants, so there is a Y% chance that undiscovered black swans exist”.

Machine learning programs can perform tasks without being explicitly programmed to do so. It involves computers learning from data provided so that they carry out certain tasks. For simple tasks assigned to computers, it is possible to program algorithms telling the machine how to execute all steps required to solve the problem at hand; on the computer’s part, no learning is needed. For more advanced tasks, it can be challenging for a human to manually create the needed algorithms. In practice, it can turn out to be more effective to help the machine develop its own algorithm, rather than having human programmers specify every needed step.

The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used.

Main Parts:

• A Decision Process: In general, machine learning algorithms are used to make a prediction or classification. Based on some input data, which can be labelled or unlabeled, your algorithm will produce an estimate about a pattern in the data.
• An Error Function: An error function serves to evaluate the prediction of the model. If there are known examples, an error function can make a comparison to assess the accuracy of the model.
• An Model Optimization Process: If the model can fit better to the data points in the training set, then weights are adjusted to reduce the discrepancy between the known example and the model estimate. The algorithm will repeat this evaluate and optimize process, updating weights autonomously until a threshold of accuracy has been met.

Types:

• Unsupervised machine learning

Unsupervised learning, also known as unsupervised machine learning, uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to discover similarities and differences in information make it the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, image and pattern recognition. It’s also used to reduce the number of features in a model through the process of dimensionality reduction; principal component analysis (PCA) and singular value decomposition (SVD) are two common approaches for this. Other algorithms used in unsupervised learning include neural networks, k-means clustering, probabilistic clustering methods, and more.

• Semi-supervised learning

Semi-supervised learning offers a happy medium between supervised and unsupervised learning. During training, it uses a smaller labeled data set to guide classification and feature extraction from a larger, unlabeled data set. Semi-supervised learning can solve the problem of having not enough labeled data (or not being able to afford to label enough data) to train a supervised learning algorithm.

• Reinforcement machine learning

Reinforcement machine learning is a behavioral machine learning model that is similar to supervised learning, but the algorithm isn’t trained using sample data. This model learns as it goes by using trial and error. A sequence of successful outcomes will be reinforced to develop the best recommendation or policy for a given problem.

Characters:

• Features must be unique and may not be found prevalent with other (different) forms:

Great features are the ones that are unique to apple and should not be applicable to other fruits. The toughness characteristic of apples such as “hard to teeth” may not be a good feature. This is because guava can also be explained using this feature.

• Features must be found in most of the data samples:

Great features represent unique characteristics that can be applied across different types of data samples and are not limited to just one data sample. For example, can the “red” color of the apple act as a feature? Not really. Because apples can be found in different colors. It might have happened that the sample of apples that was taken for evaluation contained apples of just a “red” color. If not found, we may end up creating models having high bias.

• Features in reality:

There can be features that can be accidental in nature and is not a feature at all when considering the population. For example, in a particular sample of data, a particular kind of feature can be found to be prevalent. However, when multiple data samples are taken, the feature goes missing.

Advantages of Machine Learning:

1. Automation and Efficiency:

ML automates repetitive tasks, saving time and effort in data analysis, decision-making, and problem-solving.

2. Data Mining:

ML algorithms can discover patterns and insights from large datasets, helping businesses make informed decisions.

3. Accuracy and Precision:

ML models can provide accurate predictions and classifications, especially when trained on sufficient and representative data.

4. Adaptability:

ML models can adapt to changing conditions and learn from new data, making them suitable for dynamic environments.

5. Personalization:

ML enables personalized user experiences by analyzing individual preferences and tailoring recommendations.

6. Continuous Improvement:

ML models can improve over time as they learn from more data, leading to better performance and increased accuracy.

7. Handling Complexity:

ML excels at handling complex, multidimensional data and identifying intricate patterns that may be challenging for humans.

8. Automation of Exploration:

ML algorithms can explore large datasets and identify hidden patterns, trends, and correlations that may not be apparent through manual analysis.

Disadvantages of Machine Learning:

1. Data Dependency:

ML models heavily depend on the quality and quantity of the training data. Biased or incomplete data can lead to biased or inaccurate predictions.

2. Lack of Interpretability:

Some ML models, particularly complex ones like deep neural networks, lack transparency, making it challenging to interpret how they arrive at specific decisions.

3. Overfitting:

ML models may become too specific to the training data, capturing noise rather than genuine patterns, leading to poor generalization to new data.

4. Limited Understanding of Context:

ML models may struggle to understand context, sarcasm, or cultural nuances, limiting their ability to interpret certain types of data.

5. Resource Intensive:

Training sophisticated ML models can be computationally expensive and time-consuming, requiring substantial computing power and resources.

6. Ethical Concerns:

ML models can inadvertently perpetuate biases present in training data, raising ethical concerns, especially in sensitive domains like finance, healthcare, or criminal justice.

7. Security Risks:

ML models can be vulnerable to adversarial attacks, where input data is manipulated to deceive the model, posing security risks.

8. Dependency on Skilled Experts:

Building and maintaining ML models requires expertise in machine learning, data science, and domain knowledge, creating a dependence on skilled professionals.

9. Privacy Concerns:

ML models may involve the use of personal data, raising privacy concerns. Ensuring compliance with privacy regulations is crucial.

• Unpredictability:

ML models might behave unpredictably, especially in the presence of outliers or unforeseen circumstances, making it challenging to guarantee robust performance.