9/23/2019
Reasons To Learn Probability for Machine Learning - usm systems - Medium
Reasons To Learn Probability for Machine Learning usm systems Follow Sep 23 ¡ 5 min read
https://medium.com/@usmsystems23/reasons-to-learn-probability-for-machine-learning-a17a7eb56d31
1/11
9/23/2019
Reasons To Learn Probability for Machine Learning - usm systems - Medium
Probability is the field of mathematics that measures uncertainty. This is a pillar in the field of machine learning, and it is essential to study before starting. This is misleading advice because the potential makes more sense to a learner when there is a context of the applied machine learning process. In this post, you will find out why machine learning practitioners study the possibilities to improve their skills and capabilities. After reading this post, you will know: Not everyone should learn the potential; It depends on where you are on your journey of learning machine learning. Most algorithms are designed using tools and techniques from probability such as Naive Bayes and Probabilistic Graphical Models. The maximum likelihood framework that underlies the training of many machine learning algorithms comes from the field of probability. Let’s start.
https://medium.com/@usmsystems23/reasons-to-learn-probability-for-machine-learning-a17a7eb56d31
2/11
9/23/2019
Reasons To Learn Probability for Machine Learning - usm systems - Medium
Overview This tutorial is divided into seven sections; They are: 1. Reasons for not learning probability 2. It is necessary to assess the likelihood of class membership 3. Some algorithms are designed using probability 4. Models are trained using the probabilistic framework 5. Models can be tuned with a probabilistic framework 6. Probability measurements are used to estimate model proficiency 7. One More Reason Reasons for not learning probability Before you get into the reasons why you need to learn probability, let’s start by taking a brief look at the reasons why you shouldn’t. If you are just starting out with applied machine learning I think you should not study the possibilities. https://medium.com/@usmsystems23/reasons-to-learn-probability-for-machine-learning-a17a7eb56d31
3/11
9/23/2019
Reasons To Learn Probability for Machine Learning - usm systems - Medium
It is not necessary. Some machine learning algorithms do not require an appreciation of the underlying abstract theory to use machine learning as a tool to solve problems. It was slow. If it takes months and years to study the entire field before starting machine learning, the model will delay in achieving your goals of working through attendance modeling issues. This is a huge field. Not all probability is related to theoretical machine learning, let alone applied machine learning. I recommend the width-first approach to getting started in applied machine learning. I call this the results-first approach. You start by learning and practicing the steps to work out a model attendance modeling problem end-to-end (eg how to get results) with a tool (such as Skeet-Learn and Pandas in Python). https://medium.com/@usmsystems23/reasons-to-learn-probability-for-machine-learning-a17a7eb56d31
4/11
9/23/2019
Reasons To Learn Probability for Machine Learning - usm systems - Medium
This process then provides the skeleton and context to gradually deepen your knowledge, i.e. how the algorithms work and the mathematics that ultimately counts. Once you know how to work through the attendance modeling problem, let’s look at why you can increase your awareness of probability. 1) It is necessary to assess the likelihood of class membership Classification Predictive Modeling Problems An example is where a given label is assigned. One example you know is the Iris Flowers Dataset, where we have four dimensions of a flower and the goal is to assign one of three different species of Iris flower to consideration. We can model the problem by assigning the class label directly to each observation. Input: Dimensions of a flower. Output: an iris species.
https://medium.com/@usmsystems23/reasons-to-learn-probability-for-machine-learning-a17a7eb56d31
5/11
9/23/2019
Reasons To Learn Probability for Machine Learning - usm systems - Medium
A more general approach is to formulate the problem as a potential class membership, where the probability of each known class being examined is evaluated. Input: Dimensions of a flower. Output: the probability of membership for each iris species. Formulating the problem as an estimate of class membership simplifies the modeling problem and makes the model easier to learn. This allows the model to capture the opacity in the data, which allows the user, such as the downstream process, to understand the probabilities in the domain context. By selecting the class with the largest probability, the probabilities can be transformed into a crisp class label. The probabilities can be scaled or changed using the probability calibration procedure. This choice of class membership framing requires a basic understanding of the likelihood of a modeled prediction problem description. 2) Some algorithms are designed using probability
https://medium.com/@usmsystems23/reasons-to-learn-probability-for-machine-learning-a17a7eb56d31
6/11
9/23/2019
Reasons To Learn Probability for Machine Learning - usm systems - Medium
There are specially designed algorithms to use tools and techniques from probability. These are from individual algorithms, such as the Naive Bayes algorithm, which are built using some simple ŕ°š with Bayes theory. Naive Bayes It also extends to the whole field of study, called probability graphical models, often graphical models, or abbreviated PGM, and is based around Bayes theory. Probabilistic graphical models A notable graphical model is Bayesian Belief Networks or Bayesian Networks, which can capture conditional dependencies between variables. Bayesian belief networks 3) Models are trained using the probabilistic framework Many machine learning models are trained using an iterative algorithm developed under a potential framework.
https://medium.com/@usmsystems23/reasons-to-learn-probability-for-machine-learning-a17a7eb56d31
7/11
9/23/2019
Reasons To Learn Probability for Machine Learning - usm systems - Medium
The framework of maximum likelihood estimation is probably the most common, sometimes abbreviated as MLE. It is a framework for estimating the model parameters (eg weights) given the observed data. It is a framework that represents the general least squares estimation of the linear regression model. The expectation-maximization algorithm, or EM for short, is an approach for maximum likelihood estimation that is often used for unsupervised data clustering, e.g. Estimating k for k clusters, also known as k -means clustering algorithm. For models that estimate class membership, the maximum likelihood estimation framework provides a way to reduce the difference or difference between the observed and estimated probability distribution. It is used in classification algorithms such as logistic regression and deep learning neural networks. It is common to measure this difference in the probability distribution during training using entropy, e.g. By cross entropy. The differences between the distributions measured by the entropy, and the KL divergence, and from the cross-entropy information theory are built directly on the https://medium.com/@usmsystems23/reasons-to-learn-probability-for-machine-learning-a17a7eb56d31
8/11
9/23/2019
Reasons To Learn Probability for Machine Learning - usm systems - Medium
theory of probability. For example, entropy is directly calculated as a negative log of probability. 4) Models can be tuned with a probabilistic framework It is common to tune the hyperparameters of the machine learning model, kNN fork, or the learning rate on the neural network. Typical methods include grid search ranges of hyperparameters or randomly coupled hyperparameter combinations. Bayesian optimization is more efficient for hyperparameter optimization, which searches the space of possible configurations based on those configurations that lead to improved performance. As its name suggests, this approach is designed and uses Bayes theory when designing the space of possible configurations. 5) Probability measurements are used to estimate model proficiency For predictive algorithms of probability, evaluation steps are necessary to capture the model performance. https://medium.com/@usmsystems23/reasons-to-learn-probability-for-machine-learning-a17a7eb56d31
9/11
9/23/2019
Reasons To Learn Probability for Machine Learning - usm systems - Medium
There are several steps to capture the performance of the model based on the probability given by Prob. Common examples include overall measures such as log loss and Briar score. For binary classification tasks where a single probability score is estimated, the receiver operating characteristic, or ROC, can construct curves to explore different cut-offs, which can be used in interpreting the estimation, resulting in different trade-offs. The area under the ROC curve, or ROC AUC, can also be calculated as a total measure. The selection and interpretation of these scoring methods require a foundational understanding of the theory of probability.
Machine Learning
AI
Arti cial Intelligence
Ai Development Company
Ai Development
Discover Medium
Make Medium yours
Become a member
Welcome to a place where words matter. On Medium, smart voices and original ideas take center stage - with no ads in sight. Watch
Follow all the topics you care about, and we’ll deliver the best stories for you to your homepage and inbox. Explore
Get unlimited access to the best stories on Medium — and support writers while you’re at it. Just $5/month. Upgrade
https://medium.com/@usmsystems23/reasons-to-learn-probability-for-machine-learning-a17a7eb56d31
10/11
9/23/2019
Reasons To Learn Probability for Machine Learning - usm systems - Medium
About
https://medium.com/@usmsystems23/reasons-to-learn-probability-for-machine-learning-a17a7eb56d31
Help
Legal
11/11