You can’t learn everything from books, which is why this list of ten unconventional knowledge about data analysis tools will come in handy. Business itself requires a lot of hard work but when it comes to success it has a rewarding synonym. In these pages you’ll find practical information about how to build a data mining model to turn a binary classification problem into an objective classification task, as well as advice on how to derive the probability of a disease according to given sets of symptoms.
1. Use Text Mining for finding ‘interesting’ things in text documents
In the context of our discipline, ‘interesting’ means something that could be a base for a new knowledge discovery. It has nothing to do with its literary quality or importance; it’s all about you. To get an impression about what we are talking about, check out this website , which takes some input text and tries to figure out the most interesting parts: frequently used words, etc.
2. Consider using a Support Vector Machine (SVM) in a classification task
When dealing with binary classification tasks, SVM may be a good option. You can easily feed it with training sets that are composed of positive and negative samples and let the algorithm learn by itself how to separate them. It’s all about choosing the right kernel function that defines how samples in your dataset are separated.
3. Learn about Decision trees
Decision trees are useful if you are trying to learn how to predict something based on a few variables. In other words, this approach is well suited if your task is an objective classification or prediction of something that can be somehow expressed via a set of independent variables.
4. Try out Naïve Bayes / Multinomial naive Bayes classifier
This is a really powerful tool for modeling and building decision trees for categorical variables, especially when you have more than one variable in your data set. It’s also useful when used to create multinomial naive Bayes classifiers. One of its advantages is that it assumes each variable to be treated independently. This means that if you have a training set that contains more than one categorical variable, their feature vectors will be summed up with multiplication of values and probabilities of each category.
5. Learn about graphical models
Graphical models describe exactly where variables are located in time or space. They also take into account missing values, which are particularly useful when you’re trying to predict something that has a dependent variable.
6. Use dependency modeling
In a nutshell, it’s just like regular modeling with dependent measures, but among all of your independent variables you’ll find some that are highly correlated and you’ll factor them into your model. This approach makes sense when dealing with data sets that are too complex to be modeled through traditional statistical methods.
7. Use a classification tree
Classification trees are great when you want to deal with a complex task that involves lots of measurement variables, but you’re not sure which ones are the most important. In other words, it’s useful when you need to learn how to model something without knowing the whole picture at once . Utilize the most current version of your data analysis software because you never know what it will bring to you, such as additional functionality that could come in handy when completing a complex task.
8. Use a recursive partitioning decision tree (RPART)
The idea behind RPARTs is that the data you collect can be divided into groups by certain properties (temperature, distance from a city, income group) and then these groups are used to build more precise models.
9. Use an iterative meta learning algorithm
With iterative meta learning, you can build more complex models that are able to handle more complex tasks by combining them. So, instead of using different algorithms like SVMs and decision trees, you can combine them into one model and iteratively improve it until it performs well enough.
10. Try out Support vector machines (SVMs)
SVMs are great when you want to build accurate models. They’re particularly useful when your data set includes missing values, outliers, and noisy variables. The idea behind SVMs is to build a model that can separate between positive and negative samples in your data set.
Conclusion:
These are the 10 most powerful machine learning algorithms out there. Each one of these methods is pretty complex and time consuming, but they can also be used individually to a certain extent. Either way, they’re very powerful tools that can be used in almost every situation where you need to develop models and algorithms. I hope at least a few of them were new to you!
