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C7900 | Fixed effect factor: Data has been gathered from all the levels of the factor that are of interest. Random effect factor: The factor has many possible levels, interest is in all possible levels, but only a random sample of levels is included in the data. | |
C7901 | If there are only two variables, one is continuous and another one is categorical, theoretically, it would be difficult to capture the correlation between these two variables. | |
C7902 | A point estimate of a population parameter is a single value of a statistic. For example, the sample mean x is a point estimate of the population mean μ. Similarly, the sample proportion p is a point estimate of the population proportion P. | |
C7903 | How to choose the size of the convolution filter or Kernel size1x1 kernel size is only used for dimensionality reduction that aims to reduce the number of channels. It captures the interaction of input channels in just one pixel of feature map. 2x2 and 4x4 are generally not preferred because odd-sized filters symmetrically divide the previous layer pixels around the output pixel. | |
C7904 | An n-gram model is a type of probabilistic language model for predicting the next item in such a sequence in the form of a (n − 1)–order Markov model. | |
C7905 | Discrete Probability Distributions If a random variable is a discrete variable, its probability distribution is called a discrete probability distribution. An example will make this clear. Suppose you flip a coin two times. This simple statistical experiment can have four possible outcomes: HH, HT, TH, and TT. | |
C7906 | Randomization in an experiment is where you choose your experimental participants randomly. If you use randomization in your experiments, you guard against bias. For example, selection bias (where some groups are underrepresented) is eliminated and accidental bias (where chance imbalances happen) is minimized. | |
C7907 | 1:3610:15Suggested clip · 117 secondsConducting a Multiple Regression using Microsoft Excel Data YouTubeStart of suggested clipEnd of suggested clip | |
C7908 | The derivative of the sigmoid function is the sigmoid function times one minus itself. | |
C7909 | Solve each equation to get a solution to the binomial. For x^2 - 9 = 0, for example, x - 3 = 0 and x + 3 = 0. Solve each equation to get x = 3, -3. If one of the equations is a trinomial, such as x^2 + 2x + 4 = 0, solve it using the quadratic formula, which will result in two solutions (Resource). | |
C7910 | The interpretation of the odds ratio depends on whether the predictor is categorical or continuous. Odds ratios that are greater than 1 indicate that the even is more likely to occur as the predictor increases. Odds ratios that are less than 1 indicate that the event is less likely to occur as the predictor increases. | |
C7911 | The Moment Generating Function of the Binomial Distribution (3) dMx(t) dt = n(q + pet)n−1pet = npet(q + pet)n−1. Evaluating this at t = 0 gives (4) E(x) = np(q + p)n−1 = np. | |
C7912 | Mean Absolute Error (MAE): This measures the absolute average distance between the real data and the predicted data, but it fails to punish large errors in prediction. Mean Square Error (MSE): This measures the squared average distance between the real data and the predicted data. | |
C7913 | A regression line (LSRL - Least Squares Regression Line) is a straight line that describes how a response variable y changes as an explanatory variable x changes. The line is a mathematical model used to predict the value of y for a given x. No line will pass through all the data points unless the relation is PERFECT. | |
C7914 | The basic strength of inductive reasoning is its use in predicting what might happen in the future or in establishing the possibility of what you will encounter. The main weakness of inductive reasoning is that it is incomplete, and you may reach false conclusions even with accurate observations. | |
C7915 | Initially, I started with 22,500 labeled samples and used that to create a classifier using fastText and this platform. At only 5,000 labeled samples, the transfer learning model provided an over 34% improvement in accuracy over fastText, and maintained an 86% accuracy rate. | |
C7916 | Fine-tuning deep learning involves using weights of a previous deep learning algorithm for programming another similar deep learning process. Weights are used to connect each neuron in one layer to every neuron in the next layer in the neural network. | |
C7917 | Linear regression is a linear method to model the relationship between your independent variables and your dependent variables. Advantages include how simple it is and ease with implementation and disadvantages include how is' lack of practicality and how most problems in our real world aren't “linear”. | |
C7918 | A word embedding is a learned representation for text where words that have the same meaning have a similar representation. It is this approach to representing words and documents that may be considered one of the key breakthroughs of deep learning on challenging natural language processing problems. | |
C7919 | The data used in cluster analysis can be interval, ordinal or categorical. However, having a mixture of different types of variable will make the analysis more complicated. | |
C7920 | How to Measure VariabilityThe Range. The range is the difference between the largest and smallest values in a set of values. The Interquartile Range (IQR) The interquartile range (IQR) is a measure of variability, based on dividing a data set into quartiles. The Variance. The Standard Deviation. Effect of Changing Units. | |
C7921 | Ridge regression has an additional factor called λ (lambda) which is called the penalty factor which is added while estimating beta coefficients. This penalty factor penalizes high value of beta which in turn shrinks beta coefficients thereby reducing the mean squared error and predicted error. | |
C7922 | "Correlation is not causation" means that just because two things correlate does not necessarily mean that one causes the other. Correlations between two things can be caused by a third factor that affects both of them. | |
C7923 | Let X be a discrete random variable with the Bernoulli distribution with parameter p: X∼Bern(p) Then the variance of X is given by: var(X)=p(1−p) | |
C7924 | The Pearson correlation evaluates the linear relationship between two continuous variables. The Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data. Spearman correlation is often used to evaluate relationships involving ordinal variables. | |
C7925 | Classification requires labels. Therefore you first cluster your data and save the resulting cluster labels. Then you train a classifier using these labels as a target variable. By saving the labels you effectively seperate the steps of clustering and classification. | |
C7926 | The 22 Design design where two factors (say factor A\,\! and factor B\,\!) are investigated at two levels. A single replicate of this design will require four runs ({{2}^{2}}=2\times 2=4\,\!) The effects investigated by this design are the two main effects, A\,\! and B,\,\! and the interaction effect AB\,\!. | |
C7927 | Augmented reality (AR) adds digital elements to a live view often by using the camera on a smartphone. Virtual reality (VR) implies a complete immersion experience that shuts out the physical world. | |
C7928 | The SVM typically tries to use a "kernel function" to project the sample points to high dimension space to make them linearly separable, while the perceptron assumes the sample points are linearly separable. | |
C7929 | Logistic regression is used to predict the class (or category) of individuals based on one or multiple predictor variables (x). It is used to model a binary outcome, that is a variable, which can have only two possible values: 0 or 1, yes or no, diseased or non-diseased. | |
C7930 | Definition: Random sampling is a part of the sampling technique in which each sample has an equal probability of being chosen. A sample chosen randomly is meant to be an unbiased representation of the total population. An unbiased random sample is important for drawing conclusions. | |
C7931 | Inverted Dropout is how Dropout is implemented in practice in the various deep learning frameworks because it helps to define the model once and just change a parameter (the keep/drop probability) to run train and test on the same model. | |
C7932 | Q-Learning is a value-based reinforcement learning algorithm which is used to find the optimal action-selection policy using a Q function. Our goal is to maximize the value function Q. The Q table helps us to find the best action for each state. Initially we explore the environment and update the Q-Table. | |
C7933 | In General, A Discriminative model models the decision boundary between the classes. A Generative Model explicitly models the actual distribution of each class. A Discriminative model learns the conditional probability distribution p(y|x). Both of these models were generally used in supervised learning problems. | |
C7934 | Precision and recall at k: Definition Precision at k is the proportion of recommended items in the top-k set that are relevant. Its interpretation is as follows. Suppose that my precision at 10 in a top-10 recommendation problem is 80%. This means that 80% of the recommendation I make are relevant to the user. | |
C7935 | Multi-label classification is a generalization of multiclass classification, which is the single-label problem of categorizing instances into precisely one of more than two classes; in the multi-label problem there is no constraint on how many of the classes the instance can be assigned to. | |
C7936 | Multicollinearity can also be detected with the help of tolerance and its reciprocal, called variance inflation factor (VIF). If the value of tolerance is less than 0.2 or 0.1 and, simultaneously, the value of VIF 10 and above, then the multicollinearity is problematic. | |
C7937 | The distribution pX (x) is called the target distribution, while qX (x) is the sampling distribution or the proposal distribution. | |
C7938 | Robust standard errors address the problem of errors that are not independent and identically distributed. The use of robust standard errors will not change the coefficient estimates provided by OLS, but they will change the standard errors and significance tests. | |
C7939 | 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. | |
C7940 | For a perfectly normal distribution the mean, median and mode will be the same value, visually represented by the peak of the curve. The normal distribution is often called the bell curve because the graph of its probability density looks like a bell. | |
C7941 | One way to prove Chebyshev's inequality is to apply Markov's inequality to the random variable Y = (X − μ)2 with a = (kσ)2. Chebyshev's inequality then follows by dividing by k2σ2. | |
C7942 | In statistics, the kth order statistic of a statistical sample is equal to its kth-smallest value. Together with rank statistics, order statistics are among the most fundamental tools in non-parametric statistics and inference. | |
C7943 | Random Forest Regression is a supervised learning algorithm that uses ensemble learning method for regression. A Random Forest operates by constructing several decision trees during training time and outputting the mean of the classes as the prediction of all the trees. | |
C7944 | No Normality RequiredComparison of Statistical Analysis Tools for Normally and Non-Normally Distributed DataTools for Normally Distributed DataEquivalent Tools for Non-Normally Distributed DataANOVAMood's median test; Kruskal-Wallis testPaired t-testOne-sample sign testF-test; Bartlett's testLevene's test3 more rows | |
C7945 | Continuous probability distribution: A probability distribution in which the random variable X can take on any value (is continuous). Because there are infinite values that X could assume, the probability of X taking on any one specific value is zero. The normal distribution is one example of a continuous distribution. | |
C7946 | Confounding means the distortion of the association between the independent and dependent variables because a third variable is independently associated with both. A causal relationship between two variables is often described as the way in which the independent variable affects the dependent variable. | |
C7947 | Convolutional neural networks work because it's a good extension from the standard deep-learning algorithm. Given unlimited resources and money, there is no need for convolutional because the standard algorithm will also work. However, convolutional is more efficient because it reduces the number of parameters. | |
C7948 | Image embedding refers to a set of techniques used for reduction the dimensionality of the input data processed by general NNs, including deep NNs. Image embedding refers to a set of techniques used for reduction the dimensionality of the input data processed by general NNs, including deep NNs. | |
C7949 | PD analysis is a method used by larger institutions to calculate their expected loss. A PD is assigned to each risk measure and represents as a percentage the likelihood of default. LGD represents the amount unrecovered by the lender after selling the underlying asset if a borrower defaults on a loan. | |
C7950 | Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning. | |
C7951 | The binomial is a type of distribution that has two possible outcomes (the prefix “bi” means two, or twice). For example, a coin toss has only two possible outcomes: heads or tails and taking a test could have two possible outcomes: pass or fail. A Binomial Distribution shows either (S)uccess or (F)ailure. | |
C7952 | relu . The difference is that relu is an activation function whereas LeakyReLU is a Layer defined under keras. layers . For activation functions you need to wrap around or use inside layers such Activation but LeakyReLU gives you a shortcut to that function with an alpha value. | |
C7953 | We capture the notion of being close to a number with a probability density function which is often denoted by ρ(x). If the probability density around a point x is large, that means the random variable X is likely to be close to x. If, on the other hand, ρ(x)=0 in some interval, then X won't be in that interval. | |
C7954 | In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. | |
C7955 | Ultimately, the difference between inference and prediction is one of fulfillment: while itself a kind of inference, a prediction is an educated guess (often about explicit details) that can be confirmed or denied, an inference is more concerned with the implicit. | |
C7956 | a theory that attempts to explain how imagery works in performance enhancement. It suggests that imagery develops and enhances a coding system that creates a mental blueprint of what has to be done to complete an action. | |
C7957 | Yes, there are. One example is the WEKA MOA framework [1]. This framework implements standard algorithms in the literature of concept drift detection. The nice thing about this framework is that it allows users to generate new data streams which contains concept drifts of different types. | |
C7958 | A regression line is a straight line that de- scribes how a response variable y changes as an explanatory variable x changes. We often use a regression line to predict the value of y for a given value of x. | |
C7959 | Time-series data is a set of observations collected at usually discrete and equally spaced time intervals. Cross-sectional data are observations that come from different individuals or groups at a single point in time. | |
C7960 | Statistics is generally considered a prerequisite to the field of applied machine learning. We need statistics to help transform observations into information and to answer questions about samples of observations. | |
C7961 | Mean Square Error, Quadratic loss, L2 Loss Mean Square Error (MSE) is the most commonly used regression loss function. MSE is the sum of squared distances between our target variable and predicted values. | |
C7962 | In project management terms, an s-curve is a mathematical graph that depicts relevant cumulative data for a project—such as cost or man-hours—plotted against time. An s-curve in project management is typically used to track the progress of a project. | |
C7963 | 2 Answers. If M is your matrix, then it represents a linear f:Rn→Rn, thus when you do M(T) by row times column multiplication you obtain a vectorial expression for your f(T). Thus ∂M∂T is just the derivative of the vector MT, which you do component-wise. | |
C7964 | A Turing Test is a method of inquiry in artificial intelligence (AI) for determining whether or not a computer is capable of thinking like a human being. The test is named after Alan Turing, the founder of the Turing Test and an English computer scientist, cryptanalyst, mathematician and theoretical biologist. | |
C7965 | p(x) = the likelihood that random variable takes a specific value of x. The sum of all probabilities for all possible values must equal 1. Furthermore, the probability for a particular value or range of values must be between 0 and 1. Probability distributions describe the dispersion of the values of a random variable. | |
C7966 | The biggest advantage of linear regression models is linearity: It makes the estimation procedure simple and, most importantly, these linear equations have an easy to understand interpretation on a modular level (i.e. the weights). | |
C7967 | Simple linear regression has only one x and one y variable. Multiple linear regression has one y and two or more x variables. For instance, when we predict rent based on square feet alone that is simple linear regression. | |
C7968 | Very expensive voltmeters are often made to measure “true RMS”, because that is what is desired. Low-cost voltmeters approximate the RMS value. To approximate the RMS value for a sine wave, one could simply find the peak value of the sine wave and multiply it by . | |
C7969 | Data Drift Defined Data drift is unexpected and undocumented changes to data structure, semantics, and infrastructure that is a result of modern data architectures. Data drift breaks processes and corrupts data, but can also reveal new opportunities for data use. | |
C7970 | 2:296:43Suggested clip · 121 secondsHow to calculate Confidence Intervals and Margin of Error - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7971 | Service-Level Objective (SLO) Availability, in SRE terms, defines whether a system is able to fulfill its intended function at a point in time. | |
C7972 | Conjugate priors are useful because they reduce Bayesian updating to modifying the parameters of the prior distribution (so-called hyperparameters) rather than computing integrals. | |
C7973 | Unsupervised learning algorithms are used to group cases based on similar attributes, or naturally occurring trends, patterns, or relationships in the data. These models also are referred to as self-organizing maps. Unsupervised models include clustering techniques and self-organizing maps. | |
C7974 | inter-rater reliability | |
C7975 | It is important to realize that this conclusion may or may not be correct. Our acceptance or rejection of an hypothesis, and the reality of the truth or falsity of the hypothesis, creates four possibilities, shown below. | |
C7976 | Data augmentation in data analysis are techniques used to increase the amount of data by adding slightly modified copies of already existing data or newly created synthetic data from existing data. It acts as a regularizer and helps reduce overfitting when training a machine learning model. | |
C7977 | The tensor of inertia gives us an idea about how the mass is distributed in a rigid body. Analogously, we can define the tensor of inertia about point O, by writing equation(4) in matrix form. It follows from the definition of the products of inertia, that the tensors of inertia are always symmetric. | |
C7978 | Definition: An image processing method that creates a bitonal (aka binary) image based on setting a threshold value on the pixel intensity of the original image. The thresholding process is sometimes described as separating an image into foreground values (black) and background values (white). | |
C7979 | Key Takeaways. Standard deviation looks at how spread out a group of numbers is from the mean, by looking at the square root of the variance. The variance measures the average degree to which each point differs from the mean—the average of all data points. | |
C7980 | Usually, Deep Learning takes more time to train as compared to Machine Learning. The main reason is that there are so many parameters in a Deep Learning algorithm. Whereas Machine Learning takes much less time to train, ranging from a few seconds to a few hours. | |
C7981 | There are several undeniable truths about statistics: First and foremost, they can be manipulated, massaged and misstated. Second, if bogus statistical information is repeated often enough, it eventually is considered to be true. | |
C7982 | In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if-then rules rather than through conventional procedural code. | |
C7983 | where our data set is expressed by the matrix X∈Rn×d X ∈ R n × d . Following from this equation, the covariance matrix can be computed for a data set with zero mean with C=XXTn−1 C = X X T n − 1 by using the semi-definite matrix XXT X X T . | |
C7984 | Like z-scores, t-scores are also a conversion of individual scores into a standard form. However, t-scores are used when you don't know the population standard deviation; You make an estimate by using your sample. | |
C7985 | In statistics, a Poisson distribution is a statistical distribution that shows how many times an event is likely to occur within a specified period of time. It is used for independent events which occur at a constant rate within a given interval of time. | |
C7986 | 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. | |
C7987 | Clustering is a Machine Learning technique that involves the grouping of data points. Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields. | |
C7988 | Classification accuracy is our starting point. It is the number of correct predictions made divided by the total number of predictions made, multiplied by 100 to turn it into a percentage. | |
C7989 | Statement of the Multiplication Rule In order to use the rule, we need to have the probabilities of each of the independent events. Given these events, the multiplication rule states the probability that both events occur is found by multiplying the probabilities of each event. | |
C7990 | Bayesian decision theory refers to a decision theory which is informed by Bayesian probability. It is a statistical system that tries to quantify the tradeoff between various decisions, making use of probabilities and costs. | |
C7991 | Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information theory, building upon entropy and generally calculating the difference between two probability distributions. | |
C7992 | Correlation is the concept of linear relationship between two variables. It is linear relationship nor any other relationship. Whereas correlation coefficient is a measure that measures linear relationship between two variables. | |
C7993 | Popular Answers (1) That's right that LDA is an unsupervised method. | |
C7994 | Convolution is the process of adding each element of the image to its local neighbors, weighted by the kernel. This is related to a form of mathematical convolution. | |
C7995 | To recap the differences between the two: Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning structures algorithms in layers to create an "artificial neural network” that can learn and make intelligent decisions on its own. | |
C7996 | This is the “q-value.” A p-value of 5% means that 5% of all tests will result in false positives. A q-value of 5% means that 5% of significant results will result in false positives. | |
C7997 | Define hypotheses. The test statistic is a z-score (z) defined by the following equation. z = (x - M ) / [ σ /sqrt(n) ] where x is the observed sample mean, M is the hypothesized population mean (from the null hypothesis), and σ is the standard deviation of the population. | |
C7998 | Positive feedback occurs to increase the change or output: the result of a reaction is amplified to make it occur more quickly. Some examples of positive feedback are contractions in child birth and the ripening of fruit; negative feedback examples include the regulation of blood glucose levels and osmoregulation. | |
C7999 | In marketing terms, a multi-armed bandit solution is a 'smarter' or more complex version of A/B testing that uses machine learning algorithms to dynamically allocate traffic to variations that are performing well, while allocating less traffic to variations that are underperforming. |
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