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C300 | The most commonly used metric for regression tasks is RMSE (Root Mean Square Error). This is defined as the square root of the average squared distance between the actual score and the predicted score: rmse=√∑ni=1(yi−^yi)2n. | |
C301 | Regression lossMean Square Error, Quadratic loss, L2 Loss. Mean Square Error (MSE) is the most commonly used regression loss function. Mean Absolute Error, L1 Loss. Mean Absolute Error (MAE) is another loss function used for regression models. Huber Loss, Smooth Mean Absolute Error. Log-Cosh Loss. Quantile Loss. | |
C302 | Definition. A study design that randomly assigns participants into an experimental group or a control group. As the study is conducted, the only expected difference between the control and experimental groups in a randomized controlled trial (RCT) is the outcome variable being studied. | |
C303 | In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression. | |
C304 | The Decision Analysis Process is used in support of decision making bodies to help evaluate technical, cost, and schedule issues, alternatives, and their uncertainties. Decision models have the capacity for accepting and quantifying human subjective inputs: judgments of experts and preferences of decision makers. | |
C305 | Tensorflow feature columnsTensorflow feature columns. If the tensor is a matrix, you can provide a shape expressing the dimensions.Partitioning a numerical column into a set of indicator categoricals can be done using bucketized_column :More items | |
C306 | v) Matthews Correlation Coefficient (MCC) Similar to Correlation Coefficient, the range of values of MCC lie between -1 to +1. A model with a score of +1 is a perfect model and -1 is a poor model. | |
C307 | In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard integrated circuit can be seen as a digital network of activation functions that can be "ON" (1) or "OFF" (0), depending on input. | |
C308 | You description is confusing, but it is totally possible to have test error both lower and higher than training error. A lower training error is expected when a method easily overfits to the training data, yet, poorly generalizes. | |
C309 | Tips for LSTM Input The meaning of the 3 input dimensions are: samples, time steps, and features. The LSTM input layer is defined by the input_shape argument on the first hidden layer. The input_shape argument takes a tuple of two values that define the number of time steps and features. | |
C310 | Their only difference is that the conditional probability assumes that we already know something -- that B is true. The intersection doesn't assume that we know anything. So for P(A ∩ B), we will receive a probability between 0, impossible, and 1, certain. | |
C311 | In probability theory and statistics, a collection of random variables is independent and identically distributed if each random variable has the same probability distribution as the others and all are mutually independent. This property is usually abbreviated as i.i.d. or iid or IID. | |
C312 | Qualitative Differences The population standard deviation is a parameter, which is a fixed value calculated from every individual in the population. A sample standard deviation is a statistic. This means that it is calculated from only some of the individuals in a population. | |
C313 | The key assumption in ordinal regression is that the effects of any explanatory variables are consistent or proportional across the different thresholds, hence this is usually termed the assumption of proportional odds (SPSS calls this the assumption of parallel lines but it's the same thing). | |
C314 | Ordinal logistic regression (often just called 'ordinal regression') is used to predict an ordinal dependent variable given one or more independent variables. As with other types of regression, ordinal regression can also use interactions between independent variables to predict the dependent variable. | |
C315 | 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. | |
C316 | The power of a test is the probability of rejecting the null hypothesis when it is false; in other words, it is the probability of avoiding a type II error. The power may also be thought of as the likelihood that a particular study will detect a deviation from the null hypothesis given that one exists. | |
C317 | A pseudo-random process is a process that appears to be random but is not. Pseudo-random sequences typically exhibit statistically randomness while being generated by an entirely deterministic casual process. Two dimensional Faure sequence has been taken for quasi-random number. | |
C318 | The answer is a big NO. Data science gets solutions and results to specific business problems using AI as a tool. If data science is to insights, machine learning is to predictions and artificial intelligence is to actions. | |
C319 | The hidden layers of a CNN typically consist of convolutional layers, pooling layers, fully connected layers, and normalization layers. Here it simply means that instead of using the normal activation functions defined above, convolution and pooling functions are used as activation functions. | |
C320 | Using batch normalization makes the network more stable during training. This may require the use of much larger than normal learning rates, that in turn may further speed up the learning process. — Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, 2015. | |
C321 | A normal distribution with a mean of 0 and a standard deviation of 1 is called a standard normal distribution. Areas of the normal distribution are often represented by tables of the standard normal distribution. For example, a Z of -2.5 represents a value 2.5 standard deviations below the mean. | |
C322 | The purpose of causal analysis is trying to find the root cause of a problem instead of finding the symptoms. This technique helps to uncover the facts that lead to a certain situation. | |
C323 | In statistics, a positively skewed (or right-skewed) distribution is a type of distribution in which most values are clustered around the left tail of the distribution while the right tail of the distribution is longer. | |
C324 | Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream-processing methods. The rise of lambda architecture is correlated with the growth of big data, real-time analytics, and the drive to mitigate the latencies of map-reduce. | |
C325 | A small RMSE means good prediction and large means bad model. In classification, you have (finite and countable) class labels, which do not correspond to numbers. Therefore you can not use RMSE because it is difficult to find difference between, say, label 'a' and 'b'. | |
C326 | Multilevel modelling is an approach that can be used to handle clustered or grouped data. Multilevel modelling can also be used to analyse repeated measures data. | |
C327 | Image annotation for deep learning is mainly done for object detection with more precision. 3D Cuboid Annotation, Semantic Segmentation, and polygon annotation are used to annotate the images using the right tool to make the objects well-defined in the image for neural network analysis in deep learning. | |
C328 | The formula for the Expected Value for a binomial random variable is: P(x) * X. | |
C329 | To construct a histogram, the first step is to "bin" (or "bucket") the range of values—that is, divide the entire range of values into a series of intervals—and then count how many values fall into each interval. The bins are usually specified as consecutive, non-overlapping intervals of a variable. | |
C330 | The descriptive analysis uses mainly unsupervised learning approaches for summarizing, classifying, extracting rules to answer what happens was happened in the past. While Predictive analysis is about machine learning approaches for the aim forecasting future data based on past data. | |
C331 | Batch Normalization (BatchNorm) is a widely adopted technique that enables faster and more stable training of deep neural networks (DNNs). This smoothness induces a more predictive and stable behavior of the gradients, allowing for faster training. | |
C332 | The Binomial Theorem is a quick way (okay, it's a less slow way) of expanding (or multiplying out) a binomial expression that has been raised to some (generally inconveniently large) power. For instance, the expression (3x – 2)10 would be very painful to multiply out by hand. | |
C333 | Poisson Formula. 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. The Poisson distribution has the following properties: The mean of the distribution is equal to μ . The variance is also equal to μ . | |
C334 | The scale-invariant feature transform (SIFT) is an algorithm used to detect and describe local features in digital images. The descriptors are supposed to be invariant against various transformations which might make images look different although they represent the same object(s). | |
C335 | S is known both as the standard error of the regression and as the standard error of the estimate. S represents the average distance that the observed values fall from the regression line. Conveniently, it tells you how wrong the regression model is on average using the units of the response variable. | |
C336 | Learning statistics means learning to communicate using the statistical language, solving statistical problems, drawing conclusions, and supporting conclusions by explaining the reasoning behind them. There are often different ways to solve a statistical problem. | |
C337 | LSTMs control the exposure of memory content (cell state) while GRUs expose the entire cell state to other units in the network. The LSTM unit has separate input and forget gates, while the GRU performs both of these operations together via its reset gate. | |
C338 | Hypothesis Tests of the Mean and MedianParametric tests (means)Nonparametric tests (medians)1-sample t test1-sample Sign, 1-sample Wilcoxon2-sample t testMann-Whitney testOne-Way ANOVAKruskal-Wallis, Mood's median testFactorial DOE with one factor and one blocking variableFriedman test | |
C339 | 3.1 Comparison MatrixClassification AlgorithmsAccuracyF1-ScoreNaïve Bayes80.11%0.6005Stochastic Gradient Descent82.20%0.5780K-Nearest Neighbours83.56%0.5924Decision Tree84.23%0.63083 more rows• | |
C340 | The main difference between Binomial and Poisson Distribution is that the Binomial distribution is only for a certain frame or a probability of success and the Poisson distribution is used for events that could occur a very large number of times. | |
C341 | Quota sampling is defined as a non-probability sampling method in which researchers create a sample involving individuals that represent a population. Researchers choose these individuals according to specific traits or qualities. These samples can be generalized to the entire population. | |
C342 | Another serious limitation is that practitioners need to develop new skills in seeking and appraising evidence, which takes considerable time and effort. Without these skills practitioners are prone to confirmation bias – seeing only the evidence that supports their personal experience and judgment. | |
C343 | Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others. It is also called ascertainment bias in medical fields. Sampling bias limits the generalizability of findings because it is a threat to external validity, specifically population validity.AP ۱۳۹۹ غویی ۳۱ | |
C344 | This approach involves either forward selection, adding features one at a time, or backward selection, removing features one at a time until some criterion is reached. Additionally, a bidirectional selection method is available that involves adding or removing a feature at each step. | |
C345 | The calibration module allows you to better calibrate the probabilities of a given model, or to add support for probability prediction. Well calibrated classifiers are probabilistic classifiers for which the output of the predict_proba method can be directly interpreted as a confidence level. | |
C346 | The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. It is also known as the Gaussian distribution and the bell curve. | |
C347 | Basically CV<10 is very good, 10-20 is good, 20-30 is acceptable, and CV>30 is not acceptable. | |
C348 | Image features, such as edges and interest points, provide rich information on the image content. They correspond to local regions in the image and are fun- damental in many applications in image analysis: recognition, matching, recon- struction, etc. | |
C349 | The reason that SVMs often outperform ANNs in practice is that they deal with the biggest problem with ANNs, SVMs are less prone to overfitting. | |
C350 | The initial task of image processing is to enhance the quality of digital images for further analysis. This chapter also reviews methods that are used to quantitatively determine specific image information, such as relative composition, particle size, interparticle distance, intensity profile, etc. | |
C351 | Random forest (RF) missing data algorithms are an attractive approach for imputing missing data. They have the desirable properties of being able to handle mixed types of missing data, they are adaptive to interactions and nonlinearity, and they have the potential to scale to big data settings. | |
C352 | Error correction rules were initially proposed as ad hoc rules for single unit training. These rules essentially drive the output error of a given unit to zero. We start with the classical perceptron learning rule and give a proof for its convergence. | |
C353 | In Logic, the Fallacy of Division is a fallacy of induction that occurs when someone assumes that what is true of a whole, must also be true of the parts of the parts. For example, it might be that an excellent baseball team is composed of mediocre players. | |
C354 | In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. | |
C355 | In probability theory and statistics, the hypergeometric distribution is a discrete probability distribution that describes the probability of successes (random draws for which the object drawn has a specified feature) in draws, without replacement, from a finite population of size that contains exactly objects with | |
C356 | Imbalanced classification refers to a classification predictive modeling problem where the number of examples in the training dataset for each class label is not balanced. That is, where the class distribution is not equal or close to equal, and is instead biased or skewed. | |
C357 | Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. | |
C358 | Conditional probability is defined as the likelihood of an event or outcome occurring, based on the occurrence of a previous event or outcome. Conditional probability is calculated by multiplying the probability of the preceding event by the updated probability of the succeeding, or conditional, event. | |
C359 | In mathematics, input and output are terms that relate to functions. Both the input and output of a function are variables, which means that they change. You can choose the input variables yourself, but the output variables are always determined by the rule established by the function. | |
C360 | To then oversample, take a sample from the dataset, and consider its k nearest neighbors (in feature space). To create a synthetic data point, take the vector between one of those k neighbors, and the current data point. Multiply this vector by a random number x which lies between 0, and 1. | |
C361 | Artificial intelligence (AI) is the attempt to let computers perform services for which humans need intelligence. However, this is still not possible today. AI systems are capable of recognizing patterns, learning and making decisions. | |
C362 | Positive feedback loops enhance or amplify changes; this tends to move a system away from its equilibrium state and make it more unstable. Negative feedbacks tend to dampen or buffer changes; this tends to hold a system to some equilibrium state making it more stable. | |
C363 | Jakob Bernoulli | |
C364 | Programming is the fundamental requirement of deep learning. You can't perform deep learning without using a programming language. Deep learning professionals use Python or R as their programming language because of their functionalities and effectiveness. | |
C365 | A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data. See Get ONNX models for Windows ML for more information. | |
C366 | The matrix norm is similar to the magnitude of a vector. It is useful whenever a system/problem can be formulated into a matrix that has some physical meaning. | |
C367 | k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. | |
C368 | To conclude, the important thing to remember about the odds ratio is that an odds ratio greater than 1 is a positive association (i.e., higher number for the predictor means group 1 in the outcome), and an odds ratio less than 1 is negative association (i.e., higher number for the predictor means group 0 in the outcome | |
C369 | the limiting value C is 1 + A times larger than the initial output y(0)A is the number of times that the initial population must grow to reach C.if B is positive, the logistic function will always increase,while if B is negative, the function will always decrease. | |
C370 | No, because the sample is not representative of the whole population. Find the range, variance, and standard deviation for the sample data. | |
C371 | Top N accuracy — Top N accuracy is when you measure how often your predicted class falls in the top N values of your softmax distribution. | |
C372 | Class Boundaries. Separate one class in a grouped frequency distribution from another. The boundaries have one more decimal place than the raw data and therefore do not appear in the data. There is no gap between the upper boundary of one class and the lower boundary of the next class. | |
C373 | Discrete random variables can only take on values from a countable set of numbers such as the integers or some subset of integers. (Usually, they can't be fractions.) | |
C374 | The central limit theorem states that the sampling distribution of the mean approaches a normal distribution, as the sample size increases. Therefore, as a sample size increases, the sample mean and standard deviation will be closer in value to the population mean μ and standard deviation σ . | |
C375 | 1:357:43Suggested clip · 113 secondsProbability of the Complement of an Event 128-1.4 - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C376 | Bias can damage research, if the researcher chooses to allow his bias to distort the measurements and observations or their interpretation. When faculty are biased about individual students in their courses, they may grade some students more or less favorably than others, which is not fair to any of the students. | |
C377 | Definition: Distribution means to spread the product throughout the marketplace such that a large number of people can buy it. Distribution involves doing the following things: Tracking the places where the product can be placed such that there is a maximum opportunity to buy it. | |
C378 | Translational Invariance makes the CNN invariant to translation. Invariance to translation means that if we translate the inputs the CNN will still be able to detect the class to which the input belongs. Translational Invariance is a result of the pooling operation. | |
C379 | The expected value of the difference between all possible sample means is equal to the difference between population means. Thus, E(x1 - x2) = μd = μ1 - μ2. | |
C380 | It will be easier to learn and use. If you are in the industry where you need to deploy models in production, Tensorflow is your best choice. You can use Keras/Pytorch for prototyping if you want. But you don't need to switch as Tensorflow is here to stay. | |
C381 | Machine learning can be described in many ways. Perhaps the most useful is as type of optimization. This is done via what is known as an objective function, with “objective” used in the sense of a goal. This function, taking data and model parameters as arguments, can be evaluated to return a number. | |
C382 | The correlation coefficient is a statistical measure of the strength of the relationship between the relative movements of two variables. The values range between -1.0 and 1.0. A calculated number greater than 1.0 or less than -1.0 means that there was an error in the correlation measurement. | |
C383 | At a bare minimum, collect around 1000 examples. For most "average" problems, you should have 10,000 - 100,000 examples. For “hard” problems like machine translation, high dimensional data generation, or anything requiring deep learning, you should try to get 100,000 - 1,000,000 examples. | |
C384 | Independent Variables An independent variable is the factor that has some influence or impact on the dependent variable. | |
C385 | The response variable is the focus of a question in a study or experiment. An explanatory variable is one that explains changes in that variable. It can be anything that might affect the response variable. | |
C386 | Advantages of Recurrent Neural Network It is useful in time series prediction only because of the feature to remember previous inputs as well. This is called Long Short Term Memory. Recurrent neural network are even used with convolutional layers to extend the effective pixel neighborhood. | |
C387 | The uniform distribution defines equal probability over a given range for a continuous distribution. For this reason, it is important as a reference distribution. One of the most important applications of the uniform distribution is in the generation of random numbers. | |
C388 | Stochastic vs. In general, stochastic is a synonym for random. For example, a stochastic variable is a random variable. A stochastic process is a random process. Typically, random is used to refer to a lack of dependence between observations in a sequence. | |
C389 | Definition. Data Partitioning is the technique of distributing data across multiple tables, disks, or sites in order to improve query processing performance or increase database manageability. | |
C390 | Depending on the skill being taught, backward chaining has a distinct advantage: It directly links the independent completion of a task to the immediate reward or reinforcement. Once the child can complete the last step independently, he or she can work on also completing the next-to-last step independently. | |
C391 | If you have two independent groups, and the variances are equal, F = t^2. The value of “t” is then calculated as the difference between the two sample means divided by the estimated pooled sample standard deviation (in the case of two independent samples, drawn from populations of equal variance). | |
C392 | The multivariate normal distribution has two or more random variables — so the bivariate normal distribution is actually a special case of the multivariate normal distribution. | |
C393 | An important problem that arises when we search for similar items of any kind is that there may be far too many pairs of items to test each pair for their degree of similarity, even if computing the similarity of any one pair can be made very easy. | |
C394 | One assumption of Poisson Models is that the mean and the variance are equal, but this assumption is often violated. This can be dealt with by using a dispersion parameter if the difference is small or a negative binomial regression model if the difference is large. | |
C395 | Big data might be big business, but overzealous data mining can seriously destroy your brand. As companies become experts at slicing and dicing data to reveal details as personal as mortgage defaults and heart attack risks, the threat of egregious privacy violations grows. | |
C396 | One of the major advantages of neural nets is their ability to generalize. This means that a trained net could classify data from the same class as the learning data that it has never seen before. The training set is used to train a neural net. The error of this dataset is minimized during training. | |
C397 | Cross-entropy can be calculated using the probabilities of the events from P and Q, as follows: H(P, Q) = – sum x in X P(x) * log(Q(x)) | |
C398 | The function fX(x) gives us the probability density at point x. It is the limit of the probability of the interval (x,x+Δ] divided by the length of the interval as the length of the interval goes to 0. Remember that P(x<X≤x+Δ)=FX(x+Δ)−FX(x). =dFX(x)dx=F′X(x),if FX(x) is differentiable at x. | |
C399 | Reinforcement Learning(RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. |
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