Machine Learning is a complex subject and can be difficult to understand. This is why We created a Machine Learning Cheat Sheet to assist you. You are the de facto guide. This ML cheat sheet provides a useful overview of the most common machine-learning models and information about their advantages and drawbacks.
Predictive analytics aims to help users make future predictions based on data previously collected. It has two stages.
- Create a model using training examples from the training phase.
- The prediction phase: Use the model to predict an upcoming result or unanticipated outcome.
A few process maps and tables of machine-learning algorithms are now available. The most comprehensive ones were chosen for inclusion.
1. Supervised Learning
The supervised-learning models are used to find patterns in previously seen data and to map inputs to outputs. Regression models, which attempt to predict continuous variables, like stock prices, or classification models, which attempt to predict binary or multi-class factors, such as whether a customer will churn, are two examples of supervised-learning models. In the following section, we’ll be discussing two popular supervised learning models: tree-based models and linear models.
A Linear Model
Linear models are a good way to predict unknown data. Linear models simply produce a linear combination or combinations of characteristics. In this section, we will discuss the most common linear regression model in machine learning. We also highlight their drawbacks and benefits.
A simple formula to simulate the linear relationship between an input variable and a continuous output variable.
- Stock Price Forecast
- Trends in housing prices
- Customer lifetime value forecasting
- Explicit procedure
- The output coefficient can help you understand the results.
- You can be trained faster than other machine learning models
- Assume inputs and outputs will be linear
- Be observant for anomalies
- Can you underfit data with small-scale, low-dimensional data?
To put it simply, Tree-based models use a set of “if-then” rules to extrapolate predictions from the decision tree. We’ll be describing the most common linear models used in machine learning. This section will discuss their advantages and drawbacks.
Decision Tree models can be used to predict the future by applying decision rules. It can be applied to regression or classification.
- Forecast for customer churn
- Modeling credit scores
- Prognosis for disease
- Explicative and understandable
- Accepts missing values
- Overfitting is a common trait
- Be observant for anomalies
2. Unsupervised Learning
The goal of Unsupervised Learning is to identify large trends in data. Most well-known is Clustering, or segmentation between customers and users. This type of segmentation can be used in a variety of ways, including in papers, businesses, and genomes. Unsupervised learning can be seen in clustering techniques, which are able to group similar data points together, or association algorithms, which combine different data points according to pre-established rules.
K-Means is the most widely used clustering method. It establishes K groups based on Euclidean distance.
- Segmenting customers
- System of recommendations
- Supports big datasets
- It is easy to use and understand
- Produces compact clusters
- Demands the anticipated number of clusters starting at
- Problems with a variety of cluster sizes and intensities
A rule-based approach that relies on prior knowledge about the characteristics of frequently used item sets in order to identify the most itemsets within a dataset
- Insertion of product
- Engine recommendations
- Advertising optimization
- Results are easily readable and understandable
- It is an exhaustive technique because it uncovers all laws that are based on support or confidence
- This creates many dull item sets
- Memory and computation-intensive.
- This results in a lot more overlapping item sets
1. What are the top four challenges in machine learning?
Four main problems are faced by machine learning: maintaining the data (using too complex models), underfitting data (using too simple models), data scarcity, and unrepresentative sample data.
2. What questions should I ask machine learning?
- Top Interview Questions for Machine Learning
- What Types of Machine Learning are There?
- What is excessive fitting and how can it be avoided?
- What does the term “test Set” and “training Set” in a machine-learning model mean?
- How should missing or invalid data be handled in a dataset?
3. What is a machine-learning cheat sheet?
The Azure Machine Learning Algorithm Cheat sheet makes it easier to choose the right algorithm for your predictive analytics model. Machine Learning contains a vast collection of algorithms, including those for classification, recommender systems, and clustering as well as outlier detection, regression, and text processing families.
4. What fundamental ideas underlie machine learning?
Unsupervised learning and supervised learning are the primary subfields in machine learning. These concepts are closer to what we want to do with the data, even though it might seem like the first applies to prediction with human involvement.
5. What is machine learning bias?
What is machine learning bias? Bias is when an algorithm’s output is biased in favor or against one idea. Machine learning models can experience bias due to false assumptions made during the ML process.
6. How does machine learning work?
Machine learning, as it is simply defined, allows users to send large amounts of data to computer algorithms that then analyze, recommend and decide using the data only.