AI News, Machine Learning Algorithms: Which One to Choose for Your Problem

Machine Learning Algorithms: Which One to Choose for Your Problem

First of all, you should distinguish 4 types of Machine Learning tasks: Supervised learning is the task of inferring a function from labeled training data.

By fitting to the labeled training set, we want to find the most optimal model parameters to predict unknown labels on other objects (test set).

The method allows us to significantly improve accuracy, because we can use unlabeled data in the train set with a small amount of labeled data.

RL is an area of machine learning concerned with how software agents ought to take actions in some environment to maximize some notion of cumulative reward.

Now that we have some intuition about types of machine learning tasks, let’s explore the most popular algorithms with their applications in real life.

Your goal is to find the most optimal weights w1,…wn and bias for these features according to some loss function, for example, MSE or MAE for a regression problem.

In the case of MSE there is a mathematical equation from the least squares method: In practice, it’s easier to optimize it with gradient descent, that is much more computationally efficient.

Despite the simplicity of this algorithm, it works pretty well when you have thousands of features, for example, bag of words or n-gramms in text analysis.

More complex algorithms suffer from overfitting many features and not huge datasets, while linear regression provides decent quality.

Since this algorithm calculates the probability of belonging to each class, you should take into account how much the probability differs from 0 or 1 and average it over all objects as we did with linear regression.

If y equals 0, then the first addend under sum equals 0 and the second is the less the closer our predicted y_pred to 0 according to the properties of the logarithm.

It takes linear combination of features and applies non-linear function (sigmoid) to it, so it’s a very very small instance of neural network!

In regression trees we minimize the sum of a squared error between the predictive variable of the target values of the points that fall in that region and the one we assign to it.

Secondly, the result depends on the points randomly chosen at the beginning and the algorithm doesn’t guarantee that we’ll achieve the global minimum of the functional.

You have no chance to remember all the information, but you want to maximize information that you can remember in the time available, for example, learning first the theorems that occur in many exam tickets and so on.

hope that I could explain to you common perceptions of the most used machine learning algorithms and give intuition on how to choose one for your specific problem.

Supervised learning

Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.[1] It infers a function from labeled training data consisting of a set of training examples.[2] In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal).

A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances.

This requires the learning algorithm to generalize from the training data to unseen situations in a 'reasonable' way (see inductive bias).

There is no single learning algorithm that works best on all supervised learning problems (see the No free lunch theorem).

The prediction error of a learned classifier is related to the sum of the bias and the variance of the learning algorithm.[4] Generally, there is a tradeoff between bias and variance.

A key aspect of many supervised learning methods is that they are able to adjust this tradeoff between bias and variance (either automatically or by providing a bias/variance parameter that the user can adjust).

The second issue is the amount of training data available relative to the complexity of the 'true' function (classifier or regression function).

If the true function is simple, then an 'inflexible' learning algorithm with high bias and low variance will be able to learn it from a small amount of data.

But if the true function is highly complex (e.g., because it involves complex interactions among many different input features and behaves differently in different parts of the input space), then the function will only be learnable from a very large amount of training data and using a 'flexible' learning algorithm with low bias and high variance.

If the input feature vectors have very high dimension, the learning problem can be difficult even if the true function only depends on a small number of those features.

Hence, high input dimensionality typically requires tuning the classifier to have low variance and high bias.

In practice, if the engineer can manually remove irrelevant features from the input data, this is likely to improve the accuracy of the learned function.

In addition, there are many algorithms for feature selection that seek to identify the relevant features and discard the irrelevant ones.

This is an instance of the more general strategy of dimensionality reduction, which seeks to map the input data into a lower-dimensional space prior to running the supervised learning algorithm.

fourth issue is the degree of noise in the desired output values (the supervisory target variables).

If the desired output values are often incorrect (because of human error or sensor errors), then the learning algorithm should not attempt to find a function that exactly matches the training examples.

You can overfit even when there are no measurement errors (stochastic noise) if the function you are trying to learn is too complex for your learning model.

In such a situation, the part of the target function that cannot be modeled 'corrupts' your training data - this phenomenon has been called deterministic noise.

In practice, there are several approaches to alleviate noise in the output values such as early stopping to prevent overfitting as well as detecting and removing the noisy training examples prior to training the supervised learning algorithm.

There are several algorithms that identify noisy training examples and removing the suspected noisy training examples prior to training has decreased generalization error with statistical significance.[5][6] Other factors to consider when choosing and applying a learning algorithm include the following: When considering a new application, the engineer can compare multiple learning algorithms and experimentally determine which one works best on the problem at hand (see cross validation).

Given fixed resources, it is often better to spend more time collecting additional training data and more informative features than it is to spend extra time tuning the learning algorithms.

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For example, naive Bayes and linear discriminant analysis are joint probability models, whereas logistic regression is a conditional probability model.

empirical risk minimization and structural risk minimization.[7] Empirical risk minimization seeks the function that best fits the training data.

In both cases, it is assumed that the training set consists of a sample of independent and identically distributed pairs,

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This can be estimated from the training data as In empirical risk minimization, the supervised learning algorithm seeks the function

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contains many candidate functions or the training set is not sufficiently large, empirical risk minimization leads to high variance and poor generalization.

The regularization penalty can be viewed as implementing a form of Occam's razor that prefers simpler functions over more complex ones.

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The training methods described above are discriminative training methods, because they seek to find a function

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Machine Learning Algorithms: Which One to Choose for Your Problem

First of all, you should distinguish 4 types of Machine Learning tasks: Supervised learning is the task of inferring a function from labeled training data.

By fitting to the labeled training set, we want to find the most optimal model parameters to predict unknown labels on other objects (test set).

The method allows us to significantly improve accuracy, because we can use unlabeled data in the train set with a small amount of labeled data.

RL is an area of machine learning concerned with how software agents ought to take actions in some environment to maximize some notion of cumulative reward.

Now that we have some intuition about types of machine learning tasks, let’s explore the most popular algorithms with their applications in real life.

Your goal is to find the most optimal weights w1,…wn and bias for these features according to some loss function, for example, MSE or MAE for a regression problem.

In the case of MSE there is a mathematical equation from the least squares method: In practice, it’s easier to optimize it with gradient descent, that is much more computationally efficient.

Despite the simplicity of this algorithm, it works pretty well when you have thousands of features, for example, bag of words or n-gramms in text analysis.

More complex algorithms suffer from overfitting many features and not huge datasets, while linear regression provides decent quality.

Since this algorithm calculates the probability of belonging to each class, you should take into account how much the probability differs from 0 or 1 and average it over all objects as we did with linear regression.

If y equals 0, then the first addend under sum equals 0 and the second is the less the closer our predicted y_pred to 0 according to the properties of the logarithm.

It takes linear combination of features and applies non-linear function (sigmoid) to it, so it’s a very very small instance of neural network!

In regression trees we minimize the sum of a squared error between the predictive variable of the target values of the points that fall in that region and the one we assign to it.

Secondly, the result depends on the points randomly chosen at the beginning and the algorithm doesn’t guarantee that we’ll achieve the global minimum of the functional.

You have no chance to remember all the information, but you want to maximize information that you can remember in the time available, for example, learning first the theorems that occur in many exam tickets and so on.

hope that I could explain to you common perceptions of the most used machine learning algorithms and give intuition on how to choose one for your specific problem.

List of datasets for machine learning research

Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets.[1]

High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data.

In Machine Learning , different kind of datasets [ image classification, bounding box, polygon bounding, segmentation to name a few] require different kind of annotation tools.

As datasets come in myriad formats and can sometimes be difficult to use, there has been considerable work put into curating and standardizing the format of datasets to make them easier to use for machine learning research.

Machine learning tasks

When building a machine learning model, you first need to define what you are hoping to achieve with your data.

supervised machine learning task that is used to predict which of two classes (categories) an instance of data belongs to.

supervised machine learning task that is used to predict the value of the label from a set of related features.

Regression algorithms model the dependency of the label on its related features to determine how the label will change as the values of the features are varied.

The output of a regression algorithm is a function, which you can use to predict the label value for any new set of input features.

Examples of regression scenarios include: An unsupervised machine learning task that is used to group instances of data into clusters that contain similar characteristics.

The Best Way to Prepare a Dataset Easily

In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it).

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