_id stringlengths 2 6 | text stringlengths 3 395 | title stringclasses 1 value |
|---|---|---|
C1200 | a description of the effect of two or more predictor variables on an outcome variable that allows for interaction effects among the predictors. This is in contrast to an additive model, which sums the individual effects of several predictors on an outcome. | |
C1201 | Naive Bayes is a Supervised Machine Learning algorithm based on the Bayes Theorem that is used to solve classification problems by following a probabilistic approach. It is based on the idea that the predictor variables in a Machine Learning model are independent of each other. | |
C1202 | Each node in the decision tree works on a random subset of features to calculate the output. The random forest then combines the output of individual decision trees to generate the final output. The Random Forest Algorithm combines the output of multiple (randomly created) Decision Trees to generate the final output. | |
C1203 | Terms in this set (10)classification provides information in a shorthand form, and shorthand form leads to: when you simplify through classification, you inevitably lose: Although things are improving, there can still be a stigma (disgrace) associated with having a psychiatric diagnosis (T/F)More items | |
C1204 | A tensor is a generalization of vectors and matrices and is easily understood as a multidimensional array. It is a term and set of techniques known in machine learning in the training and operation of deep learning models can be described in terms of tensors. | |
C1205 | Decision trees: Are popular among non-statisticians as they produce a model that is very easy to interpret. Each leaf node is presented as an if/then rule. | |
C1206 | The probability formula is used to compute the probability of an event to occur. To recall, the likelihood of an event happening is called probability.Basic Probability Formulas.All Probability Formulas List in MathsConditional ProbabilityP(A | B) = P(A∩B) / P(B)Bayes FormulaP(A | B) = P(B | A) ⋅ P(A) / P(B)5 more rows | |
C1207 | The hazard rate refers to the rate of death for an item of a given age (x). It is part of a larger equation called the hazard function, which analyzes the likelihood that an item will survive to a certain point in time based on its survival to an earlier time (t). | |
C1208 | There are 4 types of random sampling techniques:Simple Random Sampling. Simple random sampling requires using randomly generated numbers to choose a sample. Stratified Random Sampling. Cluster Random Sampling. Systematic Random Sampling. | |
C1209 | An efficient portfolio is either a portfolio that offers the highest expected return for a given level of risk, or one with the lowest level of risk for a given expected return. The efficient frontier represents that set of portfolios that has the maximum rate of return for every given level of risk. | |
C1210 | NLP is short for natural language processing while NLU is the shorthand for natural language understanding. They share a common goal of making sense of concepts represented in unstructured data, like language, as opposed to structured data like statistics, actions, etc. | |
C1211 | Logistic regression is a powerful machine learning algorithm that utilizes a sigmoid function and works best on binary classification problems, although it can be used on multi-class classification problems through the “one vs. all” method. Logistic regression (despite its name) is not fit for regression tasks. | |
C1212 | The Skills You Need to Work in Artificial IntelligenceMath: statistics, probability, predictions, calculus, algebra, Bayesian algorithms and logic.Science: physics, mechanics, cognitive learning theory, language processing.Computer science: data structures, programming, logic and efficiency. | |
C1213 | A Simultaneous Equation Model (SEM) is a model in the form of a set of linear simultaneous equations. The system is jointly determined by the equations in the system; In other words, the system exhibits some type of simultaneity or “back and forth” causation between the X and Y variables. | |
C1214 | The correlation, denoted by r, measures the amount of linear association between two variables. The R-squared value, denoted by R 2, is the square of the correlation. It measures the proportion of variation in the dependent variable that can be attributed to the independent variable. | |
C1215 | The percentage tells you what percentage of data to remove. For example, with a 5% trimmed mean, the lowest 5% and highest 5% of the data are excluded. The mean is calculated from the remaining 90% of data points. | |
C1216 | 1:136:50Suggested clip · 113 secondsZ Scores and Normal Distributions (Example Problems) - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C1217 | Here are four common types of a learning curve and what they mean:Diminishing-Returns Learning Curve. The rate of progression increases rapidly at the beginning and then decreases over time. Increasing-Returns Learning Curve. Increasing-Decreasing Return Learning Curve (the S-curve) Complex Learning Curve. | |
C1218 | A hierarchical clustering is a set of nested clusters that are arranged as a tree. K Means clustering is found to work well when the structure of the clusters is hyper spherical (like circle in 2D, sphere in 3D). Hierarchical clustering don't work as well as, k means when the shape of the clusters is hyper spherical. | |
C1219 | The logit model uses something called the cumulative distribution function of the logistic distribution. The probit model uses something called the cumulative distribution function of the standard normal distribution to define f(∗). Both functions will take any number and rescale it to fall between 0 and 1. | |
C1220 | Leaky ReLU. Leaky ReLUs are one attempt to fix the “dying ReLU” problem. Instead of the function being zero when x < 0, a leaky ReLU will instead have a small negative slope (of 0.01, or so). | |
C1221 | Origin of the Term The term “Receiver Operating Characteristic” has its roots in World War II. ROC curves were originally developed by the British as part of the “Chain Home” radar system. ROC analysis was used to analyze radar data to differentiate between enemy aircraft and signal noise (e.g. flocks of geese). | |
C1222 | Probabilistic data structures are a group of data structures that are extremely useful for big data and streaming applications. Generally speaking, these data structures use hash functions to randomize and compactly represent a set of items. | |
C1223 | 10 Ways to Improve Your Machine Learning ModelsStudying learning curves. As a first step to improving your results, you need to determine the problems with your model. Using cross-validation correctly. Choosing the right error or score metric. Searching for the best hyper-parameters. Testing multiple models. Averaging models. Stacking models. Applying feature engineering.More items | |
C1224 | Digital image processing is the use of computer algorithms to perform image processing on digital images . Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression. | |
C1225 | The three main methods to perform linear regression analysis in Excel are: Regression tool included with Analysis ToolPak. Scatter chart with a trendline. | |
C1226 | The marks for a group of students before (pre) and after (post) a teaching intervention are recorded below: Marks are continuous (scale) data. Continuous data are often summarised by giving their average and standard deviation (SD), and the paired t-test is used to compare the means of the two samples of related data. | |
C1227 | Parameters are key to machine learning algorithms. In this case, a parameter is a function argument that could have one of a range of values. In machine learning, the specific model you are using is the function and requires parameters in order to make a prediction on new data. | |
C1228 | Active Learning StrategiesGroup Activities. Case-based learning. Case-based learning requires students to apply their knowledge to reach a conclusion about an open-ended, real-world situation. Individual Activities. Application cards. Partner Activities. Role playing. Visual Organizing Activities. Categorizing grids. | |
C1229 | The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement. It is used in applied machine learning to estimate the skill of machine learning models when making predictions on data not included in the training data. | |
C1230 | Simple linear regression relates X to Y through an equation of the form Y = a + bX. Both quantify the direction and strength of the relationship between two numeric variables. The correlation squared (r2 or R2) has special meaning in simple linear regression. | |
C1231 | hamming distance | |
C1232 | No, it does not establish the divergence of an alternating series unless it fails the test by violating the condition limn→∞bn=0 , which is essentially the Divergence Test; therefore, it established the divergence in this case. | |
C1233 | A statistics is ancillary if its distribution does not depend on θ. More precisely, a statistic S(X) is ancillary for Θ it its distribution is the same for all θ ∈ Θ. That is, Pθ(S(X) ∈ A) is constant for θ ∈ Θ for any set A. (Xi − ¯X)2. | |
C1234 | Overview. Algorithmic probability deals with the following questions: Given a body of data about some phenomenon that we want to understand, how can we select the most probable hypothesis of how it was caused from among all possible hypotheses and how can we evaluate the different hypotheses? | |
C1235 | For quick and visual identification of a normal distribution, use a QQ plot if you have only one variable to look at and a Box Plot if you have many. Use a histogram if you need to present your results to a non-statistical public. As a statistical test to confirm your hypothesis, use the Shapiro Wilk test. | |
C1236 | Recurrent Neural Networks(RNN) are a type of Neural Network where the output from the previous step is fed as input to the current step. RNN's are mainly used for, Sequence Classification — Sentiment Classification & Video Classification. | |
C1237 | The generalized Kronecker delta or multi-index Kronecker delta of order 2p is a type (p,p) tensor that is a completely antisymmetric in its p upper indices, and also in its p lower indices. | |
C1238 | To calculate the variance follow these steps:Work out the Mean (the simple average of the numbers)Then for each number: subtract the Mean and square the result (the squared difference).Then work out the average of those squared differences. (Why Square?) | |
C1239 | Neural network structures/arranges algorithms in layers of fashion, that can learn and make intelligent decisions on its own. Whereas in Machine learning the decisions are made based on what it has learned only. Machine learning models/methods or learnings can be two types supervised and unsupervised learnings. | |
C1240 | The most popular supervised NLP machine learning algorithms are: Support Vector Machines. Bayesian Networks. Maximum Entropy. | |
C1241 | In short, when a dependent variable is not distributed normally, linear regression remains a statistically sound technique in studies of large sample sizes. Figure 2 provides appropriate sample sizes (i.e., >3000) where linear regression techniques still can be used even if normality assumption is violated. | |
C1242 | Classical planning concentrates on problems where most actions leave most things unchanged. Think of a world consisting of a bunch of objects on a flat surface. The action of nudging an object causes that object to change its lo- cation by a vector ∆. | |
C1243 | The low R-squared graph shows that even noisy, high-variability data can have a significant trend. The trend indicates that the predictor variable still provides information about the response even though data points fall further from the regression line. To assess the precision, we'll look at prediction intervals. | |
C1244 | Gradient boosting classifiers are a group of machine learning algorithms that combine many weak learning models together to create a strong predictive model. Decision trees are usually used when doing gradient boosting. | |
C1245 | A consecutive-k-out-of-n system is a system with n components arranged either linearly or circularly, which fails if and only if at least k consecutive components fail. An (n, f, k) system further requires that the total number of failed components is less than f for the system to be working. | |
C1246 | 3 Answers. Adjusted R2 is the better model when you compare models that have a different amount of variables. The logic behind it is, that R2 always increases when the number of variables increases. Meaning that even if you add a useless variable to you model, your R2 will still increase. | |
C1247 | Three things influence the margin of error in a confidence interval estimate of a population mean: sample size, variability in the population, and confidence level. Answer: As sample size increases, the margin of error decreases. As the variability in the population increases, the margin of error increases. | |
C1248 | A (real-valued) random variable, often denoted by X (or some other capital letter), is a function mapping a probability space (S, P) into the real line R. This is shown in Figure 1. Associated with each point s in the domain S the function X assigns one and only one value X(s) in the range R. | |
C1249 | Definition of a Non-Randomized Trial. • A study where participants have been assigned to the. treatment, procedure, or intervention alternatives by a. method that is not random. | |
C1250 | As such, sparse coding is closely related to compressed sensing, but compressed sensing specifically deals with finding the sparsest solution to an under-determined set of linear equations which, as the theory shows, is the correct solution in this case with high probability. | |
C1251 | For example, the amount of time (beginning now) until an earthquake occurs has an exponential distribution. Other examples include the length of time, in minutes, of long distance business telephone calls, and the amount of time, in months, a car battery lasts. | |
C1252 | A method for solving such problems by using matrix iterations is presented. In this method a related linear eigenvalue problem describing the perturbation of the solution from a nominal approximation is solved and updated successively until convergence. | |
C1253 | Essentially, a control variable is what is kept the same throughout the experiment, and it is not of primary concern in the experimental outcome. Any change in a control variable in an experiment would invalidate the correlation of dependent variables (DV) to the independent variable (IV), thus skewing the results. | |
C1254 | Use the formula (zy)i = (yi – ȳ) / s y and calculate a standardized value for each yi. Add the products from the last step together. Divide the sum from the previous step by n – 1, where n is the total number of points in our set of paired data. The result of all of this is the correlation coefficient r. | |
C1255 | Data Preprocessing With R: Hands-On TutorialDealing with missing data.Dealing with categorical data.Splitting the dataset into training and testing sets.Scaling the features. | |
C1256 | Random initialization refers to the practice of using random numbers to initialize the weights of a machine learning model. Random initialization is one way of performing symmetry breaking, which is the act of preventing all of the weights in the machine learning model from being the same. | |
C1257 | Probit regression, also called a probit model, is used to model dichotomous or binary outcome variables. In the probit model, the inverse standard normal distribution of the probability is modeled as a linear combination of the predictors. | |
C1258 | However, experts expect that it won't be until 2060 until AGI has gotten good enough to pass a "consciousness test". In other words, we're probably looking at 40 years from now before we see an AI that could pass for a human. | |
C1259 | 0:0010:19Suggested clip · 120 secondsThe Power Spectral Density - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C1260 | The name 'variational' comes most likely from the fact that it searches for distribution q that optimizes ELBO, and this setup is kind of like in calculus of variations, a field that studies optimization over functions (for example, problems like: given a family of curves in 2D between two points, find one with | |
C1261 | The coefficient of determination (denoted by R2) is a key output of regression analysis. It is interpreted as the proportion of the variance in the dependent variable that is predictable from the independent variable. An R2 of 0 means that the dependent variable cannot be predicted from the independent variable. | |
C1262 | Suppose we conduct a Poisson experiment, in which the average number of successes within a given region is μ. Then, the Poisson probability is: P(x; μ) = (e-μ) (μx) / x! where x is the actual number of successes that result from the experiment, and e is approximately equal to 2.71828. | |
C1263 | A validation dataset is a sample of data held back from training your model that is used to give an estimate of model skill while tuning model's hyperparameters. Procedures that you can use to make the best use of validation and test datasets when evaluating your models. | |
C1264 | The probability of a specific value of a continuous random variable will be zero because the area under a point is zero. | |
C1265 | The cumulative frequency is calculated by adding each frequency from a frequency distribution table to the sum of its predecessors. The last value will always be equal to the total for all observations, since all frequencies will already have been added to the previous total. | |
C1266 | " The value(s) assigned to a population parameter based on the value of a sample statistic is called an estimate. The sample statistic used to estimate a population param-eter is called an estimator." | |
C1267 | PythonC++CUDA | |
C1268 | A random variable is a numerical description of the outcome of a statistical experiment. For a discrete random variable, x, the probability distribution is defined by a probability mass function, denoted by f(x). This function provides the probability for each value of the random variable. | |
C1269 | Task parallelism is the simultaneous execution on multiple cores of many different functions across the same or different datasets. Data parallelism (aka SIMD) is the simultaneous execution on multiple cores of the same function across the elements of a dataset. | |
C1270 | In short, the beta distribution can be understood as representing a probability distribution of probabilities- that is, it represents all the possible values of a probability when we don't know what that probability is. | |
C1271 | In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. | |
C1272 | PDF. Typically, machine learning algorithms accept parameters that can be used to control certain properties of the training process and of the resulting ML model. In Amazon Machine Learning, these are called training parameters. | |
C1273 | Ordinal logistic regression (often just called 'ordinal regression') is used to predict an ordinal dependent variable given one or more independent variables. | |
C1274 | The harmonic mean is a type of numerical average. It is calculated by dividing the number of observations by the reciprocal of each number in the series. Thus, the harmonic mean is the reciprocal of the arithmetic mean of the reciprocals. | |
C1275 | To teach an algorithm how to recognise objects in images, we use a specific type of Artificial Neural Network: a Convolutional Neural Network (CNN). Their name stems from one of the most important operations in the network: convolution. Convolutional Neural Networks are inspired by the brain. | |
C1276 | Models that are pre-trained on ImageNet are good at detecting high-level features like edges, patterns, etc. These models understand certain feature representations, which can be reused. | |
C1277 | Techniques to reduce underfitting :Increase model complexity.Increase number of features, performing feature engineering.Remove noise from the data.Increase the number of epochs or increase the duration of training to get better results. | |
C1278 | 2.3. Random forest (RF) is an ensemble classifier that uses multiple models of several DTs to obtain a better prediction performance. It creates many classification trees and a bootstrap sample technique is used to train each tree from the set of training data. | |
C1279 | Softmax is a function :) It is mainly used to normalize neural networks output to fit between zero and one. It is used to represent the certainty “probability” in the network output. | |
C1280 | Definition of outliers. An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. | |
C1281 | Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. | |
C1282 | The area under the ROC curve (AUC) results were considered excellent for AUC values between 0.9-1, good for AUC values between 0.8-0.9, fair for AUC values between 0.7-0.8, poor for AUC values between 0.6-0.7 and failed for AUC values between 0.5-0.6. | |
C1283 | Cosine Proximity / Cosine Similarity Cosine similarity is a measure of similarity between two vectors. The mathematical representation is — — given two vectors A and B, where A represents the prediction vector and B represents the target vector. A higher cosine proximity/similarity indicates a higher accuracy. | |
C1284 | Train the model using a suitable machine learning algorithm such as SVM (Support Vector Machines), decision trees, random forest, etc. Training is the process through which the model learns or recognizes the patterns in the given data for making suitable predictions. The test set contains already predicted values. | |
C1285 | Imbalanced data typically refers to a classification problem where the number of observations per class is not equally distributed; often you'll have a large amount of data/observations for one class (referred to as the majority class), and much fewer observations for one or more other classes (referred to as the | |
C1286 | Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers. Improving operations. Many companies use predictive models to forecast inventory and manage resources. | |
C1287 | The first thing you do is use the z-score formula to figure out what the z-score is. In this case, it is the difference between 30 and 21, which is 9, divided by the standard deviation of 5, which gives you a z-score of 1.8. If you look at the z-table below, that gives you a probability value of 0.9641. | |
C1288 | In mathematics, the binary logarithm (log2 n) is the power to which the number 2 must be raised to obtain the value n. That is, for any real number x, For example, the binary logarithm of 1 is 0, the binary logarithm of 2 is 1, the binary logarithm of 4 is 2, and the binary logarithm of 32 is 5. | |
C1289 | 7 Techniques to Handle Imbalanced DataUse the right evaluation metrics. Resample the training set. Use K-fold Cross-Validation in the right way. Ensemble different resampled datasets. Resample with different ratios. Cluster the abundant class. Design your own models. | |
C1290 | We shall look at 5 popular clustering algorithms that every data scientist should be aware of.K-means Clustering Algorithm. Mean-Shift Clustering Algorithm. DBSCAN – Density-Based Spatial Clustering of Applications with Noise. EM using GMM – Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM)More items• | |
C1291 | Difference between K means and Hierarchical Clusteringk-means ClusteringHierarchical ClusteringK Means clustering needed advance knowledge of K i.e. no. of clusters one want to divide your data.In hierarchical clustering one can stop at any number of clusters, one find appropriate by interpreting the dendrogram.8 more rows• | |
C1292 | The Kappa Architecture was first described by Jay Kreps. It focuses on only processing data as a stream. It is not a replacement for the Lambda Architecture, except for where your use case fits. The idea is to handle both real-time data processing and continuous reprocessing in a single stream processing engine. | |
C1293 | A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. Restricted Boltzmann machines can also be used in deep learning networks. | |
C1294 | The input layer has its own weights that multiply the incoming data. The input layer then passes the data through the activation function before passing it on. The data is then multiplied by the first hidden layer's weights. | |
C1295 | Principal Component Analysis (PCA) is a popular dimensionality reduction technique used in Machine Learning applications. PCA condenses information from a large set of variables into fewer variables by applying some sort of transformation onto them. | |
C1296 | The 95% confidence interval (CI) is a range of values calculated from our data, that most likely, includes the true value of what we're estimating about the population. | |
C1297 | Parallel analysis is a method for determining the number of components or factors to retain from pca or factor analysis. Essentially, the program works by creating a random dataset with the same numbers of observations and variables as the original data. | |
C1298 | The modern mathematical theory of probability has its roots in attempts to analyze games of chance by Gerolamo Cardano in the sixteenth century, and by Pierre de Fermat and Blaise Pascal in the seventeenth century (for example the "problem of points"). | |
C1299 | So we need 2 things in order to apply reinforcement learning. Agent: An AI algorithm.Thus following are the steps to create an environment.Create a Simulation.Add a State vector which represents the internal state of the Simulation.Add a Reward system into the Simulation. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.