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C4600 | Loss is the penalty for a bad prediction. That is, loss is a number indicating how bad the model's prediction was on a single example. If the model's prediction is perfect, the loss is zero; otherwise, the loss is greater. | |
C4601 | Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. | |
C4602 | This is answered by examining the meaning of each term in the phrase: modal means the one that occurs most often (averages: mode), a class interval is the width of one of your groups in the frequency table or, the class interval is what you use when grouping data together, e.g., if you counted the number of pencils in | |
C4603 | It is a combination of the prior distribution and the likelihood function, which tells you what information is contained in your observed data (the “new evidence”). In other words, the posterior distribution summarizes what you know after the data has been observed. | |
C4604 | In greedy algorithm approach, decisions are made from the given solution domain. As being greedy, the closest solution that seems to provide an optimum solution is chosen. Greedy algorithms try to find a localized optimum solution, which may eventually lead to globally optimized solutions. | |
C4605 | As the maps are based on the perception of the buyer they are sometimes called perceptual maps. Positioning maps show where existing products and services are positioned in the market so that the firm can decide where they would like to place (position) their product. | |
C4606 | While PPCA is used to model a probability density of data, PLDA can be used to make probabilistic inferences about the class of data. | |
C4607 | It is generally called POS tagging. In simple words, we can say that POS tagging is a task of labelling each word in a sentence with its appropriate part of speech. We already know that parts of speech include nouns, verb, adverbs, adjectives, pronouns, conjunction and their sub-categories. | |
C4608 | The integral operator is a linear operator because it preserves two operations; the addition between functions and the multiplication of a function | |
C4609 | The idea behind bootstrap is to use the data of a sample study at hand as a “surrogate population”, for the purpose of approximating the sampling distribution of a statistic; i.e. to resample (with replacement) from the sample data at hand and create a large number of “phantom samples” known as bootstrap samples. | |
C4610 | The derivative of sigmoid(x) is defined as sigmoid(x)*(1-sigmoid(x)). Short answer : The derivative of the sigmoid function at any is implemented as because calculating the derivative this way is computationally effective. | |
C4611 | Excessive dust, spider webs, and loose sensors and detectors can all be the source of false alarms. | |
C4612 | Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the problem of finding a predictive function based on data. | |
C4613 | Bayesian learning uses Bayes' theorem to determine the conditional probability of a hypotheses given some evidence or observations. | |
C4614 | You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause, while a dependent variable is the effect. | |
C4615 | The gradient is a vector which gives us the direction in which loss function has the steepest ascent. The direction of steepest descent is the direction exactly opposite to the gradient, and that is why we are subtracting the gradient vector from the weights vector. | |
C4616 | A Classification report is used to measure the quality of predictions from a classification algorithm. The report shows the main classification metrics precision, recall and f1-score on a per-class basis. The metrics are calculated by using true and false positives, true and false negatives. | |
C4617 | Confusion matrices are used to visualize important predictive analytics like recall, specificity, accuracy, and precision. Confusion matrices are useful because they give direct comparisons of values like True Positives, False Positives, True Negatives and False Negatives. | |
C4618 | The Bayes theorem is a basis for discriminant analysis. | |
C4619 | ANSWER. A false positive means that the results say you have the condition you were tested for, but you really don't. With a false negative, the results say you don't have a condition, but you really do. | |
C4620 | Data visualization helps to tell stories by curating data into a form easier to understand, highlighting the trends and outliers. A good visualization tells a story, removing the noise from data and highlighting the useful information. | |
C4621 | Consider statistics as a problem-solving process and examine its four components: asking questions, collecting appropriate data, analyzing the data, and interpreting the results. This session investigates the nature of data and its potential sources of variation. Variables, bias, and random sampling are introduced. | |
C4622 | #8 Kronecker delta is a mixed tensor of rank two and it is invariant|TENSOR ANALYSIS. | |
C4623 | Step 1: Find the mean.Step 2: Subtract the mean from each score.Step 3: Square each deviation.Step 4: Add the squared deviations.Step 5: Divide the sum by the number of scores.Step 6: Take the square root of the result from Step 5. | |
C4624 | A continuity correction is the name given to adding or subtracting 0.5 to a discrete x-value. For example, suppose we would like to find the probability that a coin lands on heads less than or equal to 45 times during 100 flips. | |
C4625 | Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Semi-supervised learning falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data). | |
C4626 | Softmax Thus sigmoid is widely used for binary classification problems. While building a network for a multiclass problem, the output layer would have as many neurons as the number of classes in the target. For instance if you have three classes, there would be three neurons in the output layer. | |
C4627 | To review, the Forget gate decides what is relevant to keep from prior steps. The input gate decides what information is relevant to add from the current step. The output gate determines what the next hidden state should be. | |
C4628 | To predict a continuous value, you need to adjust your model (regardless whether it is Recurrent or Not) to the following conditions:Use a linear activation function for the final layer.Chose an appropriate cost function (square error loss is typically used to measure the error of predicting real values) | |
C4629 | Starting TensorBoardOpen up the command prompt (Windows) or terminal (Ubuntu/Mac)Go into the project home directory.If you are using Python virtuanenv, activate the virtual environment you have installed TensorFlow in.Make sure that you can see the TensorFlow library through Python.More items• | |
C4630 | In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. | |
C4631 | Elbow methodCompute clustering algorithm (e.g., k-means clustering) for different values of k. For each k, calculate the total within-cluster sum of square (wss).Plot the curve of wss according to the number of clusters k.More items | |
C4632 | A tensor is a generalization of vectors and matrices to potentially higher dimensions. Internally, TensorFlow represents tensors as n-dimensional arrays of base datatypes. When writing a TensorFlow program, the main object you manipulate and pass around is the tf$Tensor . | |
C4633 | Use temporal data types to store date, time, and time-interval information. Although you can store this data in character strings, it is better to use temporal types for consistency and validation. An hour, minute, and second to six decimal places (microseconds), and the time zone offset from GMT. | |
C4634 | Disparate-Treatment occurs when an employer discriminates against a specific individual or employee because of that persons race, color, national origin, sex, or religion. Disparate-Impact occurs when an employer discriminates against an entire protected class through practices, procedures, or tests. | |
C4635 | The formula for calculating a z-score is is z = (x-μ)/σ, where x is the raw score, μ is the population mean, and σ is the population standard deviation. As the formula shows, the z-score is simply the raw score minus the population mean, divided by the population standard deviation. | |
C4636 | Categorical variables represent types of data which may be divided into groups. Examples of categorical variables are race, sex, age group, and educational level. There are 8 different event categories, with weight given as numeric data. | |
C4637 | An autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data to either better understand the data set or to predict future trends. | |
C4638 | To convert a logit ( glm output) to probability, follow these 3 steps:Take glm output coefficient (logit)compute e-function on the logit using exp() “de-logarithimize” (you'll get odds then)convert odds to probability using this formula prob = odds / (1 + odds) . | |
C4639 | Dear researchers, in real world, a "reasonable" sample size for a logistic regression model is: at least 10 events (not 10 samples) per independent variable. | |
C4640 | Adam can be looked at as a combination of RMSprop and Stochastic Gradient Descent with momentum. It uses the squared gradients to scale the learning rate like RMSprop and it takes advantage of momentum by using moving average of the gradient instead of gradient itself like SGD with momentum. | |
C4641 | In context of deep learning the logits layer means the layer that feeds in to softmax (or other such normalization). The output of the softmax are the probabilities for the classification task and its input is logits layer. | |
C4642 | In probability theory and statistics, a covariance matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square matrix giving the covariance between each pair of elements of a given random vector. | |
C4643 | 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• | |
C4644 | In statistics, we usually say “random sample,” but in probability it's more common to say “IID.” Identically Distributed means that there are no overall trends–the distribution doesn't fluctuate and all items in the sample are taken from the same probability distribution. | |
C4645 | The first method involves the conditional distribution of a random variable X2 given X1. Therefore, a bivariate normal distribution can be simulated by drawing a random variable from the marginal normal distribution and then drawing a second random variable from the conditional normal distribution. | |
C4646 | Latent semantic indexing (LSI) is a concept used by search engines to discover how a term and content work together to mean the same thing, even if they do not share keywords or synonyms. Basically, though, you often need specific keywords on your pages to boost your website traffic. | |
C4647 | Correlation measures linearity between X and Y. If ρ(X,Y) = 0 we say that X and Y are “uncorrelated.” If two variables are independent, then their correlation will be 0. However, like with covariance. | |
C4648 | Noisy data is meaningless data. • It includes any data that cannot be understood and interpreted correctly by machines, such as unstructured text. • Noisy data unnecessarily increases the amount of storage space required and can also adversely affect the results of any data mining analysis. | |
C4649 | A Gaussian blur effect is typically generated by convolving an image with an FIR kernel of Gaussian values. In the first pass, a one-dimensional kernel is used to blur the image in only the horizontal or vertical direction. In the second pass, the same one-dimensional kernel is used to blur in the remaining direction. | |
C4650 | Explanation: Neural networks learn by example. They are more fault tolerant because they are always able to respond and small changes in input do not normally cause a change in output. Because of their parallel architecture, high computational rates are achieved. | |
C4651 | Autocorrelation is a characteristic of data in which the correlation between the values of the same variables is based on related objects. It violates the assumption of instance independence, which underlies most of the conventional models. | |
C4652 | The easiest way to calculate the multiple correlation coefficient (i.e. the correlation between two or more variables on the one hand, and one variable on the other) is to create a multiple linear regression (predicting the values of one variable treated as dependent from the values of two or more variables treated as | |
C4653 | To convert a frequency distribution to a probability distribution, divide area of the bar or interval of x by the total area of all the Bars. A simpler formula is: , N is the total Frequency and w is the interval of x. Example (From a frequency distribution table construct a probability plot). | |
C4654 | An autoencoder accepts input, compresses it, and then recreates the original input. A variational autoencoder assumes that the source data has some sort of underlying probability distribution (such as Gaussian) and then attempts to find the parameters of the distribution. | |
C4655 | The Exponential curve (also known as a J-curve) occurs when there is no limit to population size. The Logistic curve (also known as an S-curve) shows the effect of a limiting factor (in this case the carrying capacity of the environment). | |
C4656 | Kalman filters are used to optimally estimate the variables of interests when they can't be measured directly, but an indirect measurement is available. They are also used to find the best estimate of states by combining measurements from various sensors in the presence of noise. | |
C4657 | It's a form of machine learning and therefore a branch of artificial intelligence. Depending on the complexity of the problem, reinforcement learning algorithms can keep adapting to the environment over time if necessary in order to maximize the reward in the long-term. | |
C4658 | Binary classification refers to those classification tasks that have two class labels. Examples include: Email spam detection (spam or not). Churn prediction (churn or not). | |
C4659 | In the context of machine learning, an embedding is a low-dimensional, learned continuous vector representation of discrete variables into which you can translate high-dimensional vectors. Generally, embeddings make ML models more efficient and easier to work with, and can be used with other models as well. | |
C4660 | Multiple regression estimates how the changes in each predictor variable relate to changes in the response variable. What does it mean to control for the variables in the model? It means that when you look at the effect of one variable in the model, you are holding constant all of the other predictors in the model. | |
C4661 | Linear graphs are scaled so that equal vertical distances represent the same absolute-dollar-value change. The logarithmic scale reveals percentage changes. A change from 100 to 200, for example, is presented in the same way as a change from 1,000 to 2,000. | |
C4662 | A p-value that is calculated using an approximation to the true distribution is called an asymptotic p-value. A p-value calculated using the true distribution is called an exact p-value. For large sample sizes, the exact and asymptotic p-values are very similar. | |
C4663 | In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the | |
C4664 | Dual booting has multiple decision impacting disadvantages, below are some of the notable ones.Restart required to access the other OS. Every time you need to switch between the OS, you will have to restart the PC. Setup process is rather complicated. Not very secure. | |
C4665 | Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. | |
C4666 | In statistics, a unimodal probability distribution or unimodal distribution is a probability distribution which has a single peak. The term "mode" in this context refers to any peak of the distribution, not just to the strict definition of mode which is usual in statistics. | |
C4667 | k-means is the most widely-used centroid-based clustering algorithm. Centroid-based algorithms are efficient but sensitive to initial conditions and outliers. This course focuses on k-means because it is an efficient, effective, and simple clustering algorithm. Figure 1: Example of centroid-based clustering. | |
C4668 | In neural networks, a hidden layer is located between the input and output of the algorithm, in which the function applies weights to the inputs and directs them through an activation function as the output. In short, the hidden layers perform nonlinear transformations of the inputs entered into the network. | |
C4669 | Imbalanced data sets are a special case for classification problem where the class distribution is not uniform among the classes. Typically, they are composed by two classes: The majority (negative) class and the minority (positive) class. | |
C4670 | OCR converts images of typed or handwritten text into machine-encoded text. The major steps in image recognition process are gather and organize data, build a predictive model and use it to recognize images. | |
C4671 | Answer: Recursive function is a function which calls itself again and again. A recursive function in general has an extremely high time complexity while a non-recursive one does not. A recursive function generally has smaller code size whereas a non-recursive one is larger. | |
C4672 | In the field of machine learning, the goal of statistical classification is to use an object's characteristics to identify which class (or group) it belongs to. A linear classifier achieves this by making a classification decision based on the value of a linear combination of the characteristics. | |
C4673 | If you have both a response variable and an explanatory variable, the explanatory variable is always plotted on the x-axis (the horizontal axis). The response variable is always plotted on the y-axis (the vertical axis). | |
C4674 | RECALL is the ratio of the number of relevant records retrieved to the total number of relevant records in the database. It is usually expressed as a percentage. ──────b•d────── Page 2 PRECISION is the ratio of the number of relevant records retrieved to the total number of irrelevant and relevant records retrieved. | |
C4675 | Multilayer Perceptron (MLP) MLP is a deep learning method. A multilayer perceptron is a neural network connecting multiple layers in a directed graph, which means that the signal path through the nodes only goes one way. Each node, apart from the input nodes, has a nonlinear activation function. | |
C4676 | While precision refers to the percentage of your results which are relevant, recall refers to the percentage of total relevant results correctly classified by your algorithm. Unfortunately, it is not possible to maximize both these metrics at the same time, as one comes at the cost of another. | |
C4677 | The most efficient algorithm is one that takes the least amount of execution time and memory usage possible while still yielding a correct answer. | |
C4678 | The center of mass is the mean position of the mass in an object. Then there's the center of gravity, which is the point where gravity appears to act. For many objects, these two points are in exactly the same place. But they're only the same when the gravitational field is uniform across an object. | |
C4679 | Here eta (learning rate) and n_iter (number of iterations) are the hyperparameters that would have to be adjusted in order to obtain the best values for the model parameters a and b. | |
C4680 | Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. Optimization algorithms or strategies are responsible for reducing the losses and to provide the most accurate results possible. | |
C4681 | The "least squares" method is a form of mathematical regression analysis used to determine the line of best fit for a set of data, providing a visual demonstration of the relationship between the data points. | |
C4682 | This might sound confusing but here it goes: The p-value is the probability of observing data as extreme as (or more extreme than) your actual observed data, assuming that the Null hypothesis is true. A Type 1 Error is a false positive -- i.e. you falsely reject the (true) null hypothesis. | |
C4683 | Underfitting in Neural Networks Underfitting happens when the network is not able to generate accurate predictions on the training set—not to mention the validation set. | |
C4684 | A stochastic process is a family of random variables {Xθ}, where the parameter θ is drawn from an index set Θ. For example, let's say the index set is “time”. One example of a stochastic process that evolves over time is the number of customers (X) in a checkout line. | |
C4685 | There is no need to use LINEAR hidden layer in a neural network. Because two (or three or four) linear layers can't provide more intelligence than a single linear layer. | |
C4686 | In probability theory and statistics, the Poisson distribution (/ˈpwɑːsɒn/; French pronunciation: [pwasɔ̃]), named after French mathematician Siméon Denis Poisson, is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these | |
C4687 | This means that for each output that the decoder makes, it has access to the entire input sequence and can selectively pick out specific elements from that sequence to produce the output. Therefore, the mechanism allows the model to focus and place more “Attention” on the relevant parts of the input sequence as needed. | |
C4688 | You cannot predict the sequence of random numbers, even with a deep neural network. | |
C4689 | A normal distribution of data is one in which the majority of data points are relatively similar, meaning they occur within a small range of values with fewer outliers on the high and low ends of the data range. | |
C4690 | 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. | |
C4691 | sample is obtained by randomly selecting an individual and then selecting every kth individual from the population after the first one. | |
C4692 | In statistics a minimum-variance unbiased estimator (MVUE) or uniformly minimum-variance unbiased estimator (UMVUE) is an unbiased estimator that has lower variance than any other unbiased estimator for all possible values of the parameter. | |
C4693 | Summation of all three networks in single table:ANNSpatial RelationshipNoPerformanceANN is considered to be less powerful than CNN, RNN.ApplicationFacial recognition and Computer vision.Main advantagesHaving fault tolerance, Ability to work with incomplete knowledge.6 more rows• | |
C4694 | Therefore, the eigenvalues of A are λ = 4,−2. (λ = −2 is a repeated root of the characteristic equation.) Once the eigenvalues of a matrix (A) have been found, we can find the eigenvectors by Gaussian Elimination. to row echelon form, and solve the resulting linear system by back substitution. | |
C4695 | Some practical uses of probability distributions are: To calculate confidence intervals for parameters and to calculate critical regions for hypothesis tests. For univariate data, it is often useful to determine a reasonable distributional model for the data. | |
C4696 | The two-way linear fixed effects regression (2FE) has become a default method for estimating causal effects from panel data. Many applied researchers use the 2FE estimator to adjust for unob- served unit-specific and time-specific confounders at the same time. | |
C4697 | Binomial. The binomial distribution function specifies the number of times (x) that an event occurs in n independent trials where p is the probability of the event occurring in a single trial. It is an exact probability distribution for any number of discrete trials. | |
C4698 | A very special kind of continuous distribution is called a Normal distribution. | |
C4699 | A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. The classifier made a total of 165 predictions (e.g., 165 patients were being tested for the presence of that disease). |
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