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C6100 | Kosaraju's algorithm finds the strongly connected components of a graph. - For each vertex u of the graph do Visit(u), where Visit(u) is the recursive subroutine: - If u is unvisited then: - Mark u as visited. - For each out-neighbour v of u, do Visit(v). | |
C6101 | Multiclass classification with logistic regression can be done either through the one-vs-rest scheme in which for each class a binary classification problem of data belonging or not to that class is done, or changing the loss function to cross- entropy loss. | |
C6102 | How to Find a Sample Size Given a Confidence Interval and Width (unknown population standard deviation)za/2: Divide the confidence interval by two, and look that area up in the z-table: .95 / 2 = 0.475. E (margin of error): Divide the given width by 2. 6% / 2. : use the given percentage. 41% = 0.41. : subtract. from 1. | |
C6103 | In mathematics, a random walk is a mathematical object, known as a stochastic or random process, that describes a path that consists of a succession of random steps on some mathematical space such as the integers. | |
C6104 | Also known as implicit social cognition, implicit bias refers to the attitudes or stereotypes that affect our understanding, actions, and decisions in an unconscious manner. | |
C6105 | Rabin (1987) that uses hashing to find an exact match of a pattern string in a text. It uses a rolling hash to quickly filter out positions of the text that cannot match the pattern, and then checks for a match at the remaining positions. | |
C6106 | 0:042:26:08Suggested clip · 98 secondsStructural Equation Modeling Full Course | Structural Equation YouTubeStart of suggested clipEnd of suggested clip | |
C6107 | Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class. The class with the highest probability is considered as the most likely class. | |
C6108 | 80% accurate. Precision - Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. Recall (Sensitivity) - Recall is the ratio of correctly predicted positive observations to the all observations in actual class - yes. | |
C6109 | Box plots are useful as they show outliers within a data set. An outlier is an observation that is numerically distant from the rest of the data. When reviewing a box plot, an outlier is defined as a data point that is located outside the whiskers of the box plot. | |
C6110 | The rate of occurrence for Type I errors equals the significance level of the hypothesis test, which is also known as alpha (α). Hypothesis tests define that standard using the probability of rejecting a null hypothesis that is actually true. You set this value based on your willingness to risk a false positive. | |
C6111 | Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself. | |
C6112 | Two events are mutually exclusive if the probability of them both occurring is zero, that is if Pr(A∩B)=0. With that definition, disjoint sets are necessarily mutually exclusive, but mutually exclusive events aren't necessarily disjoint. | |
C6113 | A one hot encoding is a representation of categorical variables as binary vectors. Then, each integer value is represented as a binary vector that is all zero values except the index of the integer, which is marked with a 1. | |
C6114 | Improve your model accuracy by Transfer Learning.Loading data using python libraries.Preprocess of data which includes reshaping, one-hot encoding and splitting.Constructing the model layers of CNN followed by model compiling, model training.Evaluating the model on test data.Finally, predicting the correct and incorrect labels. | |
C6115 | SECOND RANK TENSOR PROPERTIES. Many properties are tensors that relate one vector to another or relate a scalar to a tensor. If the driving force and the response are collinear the property can be expressed as a scalar, but when that are not, the property must be expressed as a second rank tensor. | |
C6116 | Spectral analysis allows transforming a time series into its coordinates in the space of frequencies, and then to analyze its characteristics in this space. The magnitude and phase can be extracted from the coordinates. Spectral analysis is a very general method used in a variety of domains. | |
C6117 | Binomial distributions must also meet the following three criteria:The number of observations or trials is fixed. Each observation or trial is independent. The probability of success (tails, heads, fail or pass) is exactly the same from one trial to another. | |
C6118 | In Reinforcement Learning, this type of decision is called exploitation when you keep doing what you were doing, and exploration when you try something new. Naturally this raises a question about how much to exploit and how much to explore. | |
C6119 | If the biggest problem with supervised learning is the expense of labeling the training data, the biggest problem with unsupervised learning (where the data is not labeled) is that it often doesn't work very well. | |
C6120 | 2.3 Taguchi Taguchi. The Taguchi method optimizes design parameters to minimize variation before optimizing design to hit mean target values for output parameters. The Taguchi method uses special orthogonal arrays to study all the design factors with minimum of experiments. | |
C6121 | Event B is said to be independent of event A if P(A & B) The joint probability equals the sum of the marginal probabilities minus the probability that either event will occur; that is, P(A & B) = P(A) + P(B) - P(A or B). | |
C6122 | Hidden layers allow for the function of a neural network to be broken down into specific transformations of the data. Each hidden layer function is specialized to produce a defined output. | |
C6123 | The T distribution is similar to the normal distribution, just with fatter tails. Both assume a normally distributed population. T distributions have higher kurtosis than normal distributions. The probability of getting values very far from the mean is larger with a T distribution than a normal distribution. | |
C6124 | 1 : a range of medium length. 2 : the midpoint of a range (as of distance or time) 3 : a middle portion (as of a range of musical pitch) | |
C6125 | The null hypothesis of the Kruskal–Wallis test is that the mean ranks of the groups are the same. | |
C6126 | A decision tree is one of the supervised machine learning algorithms. This algorithm can be used for regression and classification problems — yet, is mostly used for classification problems. A decision tree follows a set of if-else conditions to visualize the data and classify it according to the conditions. | |
C6127 | Mathematically speaking, the regret is expressed as the difference between the payoff (reward or return) of a possible action and the payoff of the action that has been actually taken. If we denote the payoff function as u the formula becomes: regret = u(possible action) - u(action taken) | |
C6128 | Regression Techniques Regression algorithms are machine learning techniques for predicting continuous numerical values. They are supervised learning tasks which means they require labelled training examples. | |
C6129 | Use regression analysis to describe the relationships between a set of independent variables and the dependent variable. Regression analysis produces a regression equation where the coefficients represent the relationship between each independent variable and the dependent variable. | |
C6130 | Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it includes multiple such modalities. | |
C6131 | The Dirichlet distribution is a conjugate prior for the multinomial distribution. This means that if the prior distribution of the multinomial parameters is Dirichlet then the posterior distribution is also a Dirichlet distribution (with parameters different from those of the prior). | |
C6132 | More specifically the multiple linear regression fits a line through a multi-dimensional space of data points. The simplest form has one dependent and two independent variables. The dependent variable may also be referred to as the outcome variable or regressand. | |
C6133 | A box plot (also known as box and whisker plot) is a type of chart often used in explanatory data analysis to visually show the distribution of numerical data and skewness through displaying the data quartiles (or percentiles) and averages. | |
C6134 | Descriptive Analytics tells you what happened in the past. Predictive Analytics predicts what is most likely to happen in the future. Prescriptive Analytics recommends actions you can take to affect those outcomes. | |
C6135 | In machine learning, multiclass or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). | |
C6136 | For example, a collaborative filtering recommendation system for television tastes could make predictions about which television show a user should like given a partial list of that user's tastes (likes or dislikes). Note that these predictions are specific to the user, but use information gleaned from many users. | |
C6137 | Since is more degree 4 will be more complex(overfit the data) than the degree 3 model so it will again perfectly fit the data. In such case training error will be zero but test error may not be zero. | |
C6138 | An expert system is divided into two subsystems: the inference engine and the knowledge base. The knowledge base represents facts and rules. The inference engine applies the rules to the known facts to deduce new facts. Inference engines can also include explanation and debugging abilities. | |
C6139 | One common method of consolidating two probability distributions is to simply average them - for every set of values A, set If the distributions both have densities, for example, averaging the probabilities results in a probability distribution with density the average of the two input densities (Figure 1). | |
C6140 | Traditional algorithms involving face recognition work by identifying facial features by extracting features, or landmarks, from the image of the face. For example, to extract facial features, an algorithm may analyse the shape and size of the eyes, the size of nose, and its relative position with the eyes. | |
C6141 | The hazard rate measures the propensity of an item to fail or die depending on the age it has reached. It is part of a wider branch of statistics called survival analysis, a set of methods for predicting the amount of time until a certain event occurs, such as the death or failure of an engineering system or component. | |
C6142 | Real time processing requires a continual input, constant processing, and steady output of data. A great example of real-time processing is data streaming, radar systems, customer service systems, and bank ATMs, where immediate processing is crucial to make the system work properly. | |
C6143 | Events are independent if the outcome of one event does not affect the outcome of another. For example, if you throw a die and a coin, the number on the die does not affect whether the result you get on the coin. | |
C6144 | The blur, or smoothing, of an image removes “outlier” pixels that may be noise in the image. Blurring is an example of applying a low-pass filter to an image. In computer vision, the term “low-pass filter” applies to removing noise from an image while leaving the majority of the image intact. | |
C6145 | Decision trees are prone to overfitting, especially when a tree is particularly deep. This is due to the amount of specificity we look at leading to smaller sample of events that meet the previous assumptions. This small sample could lead to unsound conclusions. | |
C6146 | How to calculate the absolute error and relative errorTo find out the absolute error, subtract the approximated value from the real one: |1.41421356237 - 1.41| = 0.00421356237.Divide this value by the real value to obtain the relative error: |0.00421356237 / 1.41421356237| = 0.298% | |
C6147 | This means a fewer neurons are firing ( sparse activation ) and the network is lighter. This may never be the case in Tanh or Sigmoid. | |
C6148 | Systematic random sampling: Systematic random sampling is a method to select samples at a particular preset interval. As a researcher, select a random starting point between 1 and the sampling interval. (The number of elements in the population divided by the number of elements needed for the sample.) | |
C6149 | One drawback of boxplots is that they tend to emphasize the tails of a distribution, which are the least certain points in the data set. They also hide many of the details of the distribution. | |
C6150 | FNV-1 is rumoured to be a good hash function for strings. For long strings (longer than, say, about 200 characters), you can get good performance out of the MD4 hash function. | |
C6151 | The heuristic function is a way to inform the search about the direction to a goal. It provides an informed way to guess which neighbor of a node will lead to a goal. There is nothing magical about a heuristic function. It must use only information that can be readily obtained about a node. | |
C6152 | Classification is a machine learning concept. It is used for categorical dependent variables, where we need to classify into required groups. Logistic regression is a algorithm within classification. | |
C6153 | Graham's law states that the rate of diffusion or of effusion of a gas is inversely proportional to the square root of its molecular weight. In the same conditions of temperature and pressure, the molar mass is proportional to the mass density. | |
C6154 | Exploring the popular deep learning approach. Transfer learning is the reuse of a pre-trained model on a new problem. It's currently very popular in deep learning because it can train deep neural networks with comparatively little data. | |
C6155 | in statistics. Regression gives you the linear trend of the outcomes; residuals are the randomness that's “left over” from fitting a regression model. The correlation between the explanatory variable(s) and the residuals is/are zero because there's no linear trend left - it's been removed by the regression. | |
C6156 | The cumulative distribution function (cdf) is the probability that the variable takes a value less than or equal to x. That is. F(x) = Pr[X \le x] = \alpha. For a continuous distribution, this can be expressed mathematically as. F(x) = \int_{-\infty}^{x} {f(\mu) d\mu} | |
C6157 | From Wikipedia, the free encyclopedia. An odds ratio (OR) is a statistic that quantifies the strength of the association between two events, A and B. | |
C6158 | The sample proportion is the proportion of individuals in a sample sharing a certain trait, denoted ˆp. | |
C6159 | If the impulse is at a non-zero frequency (at ω = ω0 ) in the frequency domain (i.e. the time domain. In other words, the Fourier Transform of an everlasting exponential ejω0t is an impulse in the frequency spectrum at ω = ω0 . An everlasting exponential ejωt is a mathematical model. | |
C6160 | Fuzzy logic is used in Natural language processing and various intensive applications in Artificial Intelligence. It is extensively used in modern control systems such as expert systems. Fuzzy Logic mimics how a person would make decisions, only much faster. Thus, you can use it with Neural Networks. | |
C6161 | An estimate is unbiased if its expected value equals the true parameter value. This will be true for all sample sizes and is exact whereas consistency is asymptotic and only is approximately equal and not exact. The sample estimate of standard deviation is biased but consistent. | |
C6162 | Key differences Regression attempts to establish how X causes Y to change and the results of the analysis will change if X and Y are swapped. With correlation, the X and Y variables are interchangeable. Regression assumes X is fixed with no error, such as a dose amount or temperature setting. | |
C6163 | Blocks and strata are different. Blocking refers to classifying experimental units into blocks whereas stratification refers to classifying individuals of a population into strata. The samples from the strata in a stratified random sample can be the blocks in an experiment. | |
C6164 | William S. Gosset | |
C6165 | The p-value is a matter of convenience for us. STATA automatically takes into account the number of degrees of freedom and tells us at what level our coefficient is significant. If it is significant at the 95% level, then we have P < 0.05. | |
C6166 | Direct link to this answer The rule of thumb for Gaussian filter design is to choose the filter size to be about 3 times the standard deviation (sigma value) in each direction, for a total filter size of approximately 6*sigma rounded to an odd integer value. | |
C6167 | The output, y is generated from a normal (Gaussian) Distribution characterized by a mean and variance. In contrast to OLS, we have a posterior distribution for the model parameters that is proportional to the likelihood of the data multiplied by the prior probability of the parameters. | |
C6168 | No, moment of inertia is a tensor quantity. Sometimes it behaves as scalar & sometimes as a vector. Sometimes it depends on the directions and sometimes depends on distribution of mass of the particles in the object. | |
C6169 | Knowledge gaps can be identified by means of questionnaires or review of test scores from in training or board examinations. Correcting gaps in knowledge is important, but usually has the least impact on improving competence or performance and outcomes for patients. | |
C6170 | The mean of the negative binomial distribution with parameters r and p is rq / p, where q = 1 – p. The variance is rq / p2. The simplest motivation for the negative binomial is the case of successive random trials, each having a constant probability P of success. | |
C6171 | The main reason why we use sigmoid function is because it exists between (0 to 1). Therefore, it is especially used for models where we have to predict the probability as an output. Since probability of anything exists only between the range of 0 and 1, sigmoid is the right choice. The function is differentiable. | |
C6172 | The repetitive nearest-neighbor algorithm. The nearest-neighbor algorithm depends on what vertex you choose to start from. The repetitive nearest-neighbor algorithm says to try each vertex as starting point, and then choose the best answer. | |
C6173 | The reason n-1 is used is because that is the number of degrees of freedom in the sample. The sum of each value in a sample minus the mean must equal 0, so if you know what all the values except one are, you can calculate the value of the final one. | |
C6174 | Normalization is useful when your data has varying scales and the algorithm you are using does not make assumptions about the distribution of your data, such as k-nearest neighbors and artificial neural networks. Standardization assumes that your data has a Gaussian (bell curve) distribution. | |
C6175 | In medical testing, false negatives may provide a falsely reassuring message to patients and physicians that disease is absent, when it is actually present. This sometimes leads to inappropriate or inadequate treatment of both the patient and their disease. So, it is desired to have too many false positive. | |
C6176 | The optimal number of clusters can be defined as follow:Compute 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 | |
C6177 | Tests of hypotheses that can be made from a single sample of data were discussed on the foregoing page. As with null hypotheses, confidence intervals can be two-sided or one-sided, depending on the question at hand. | |
C6178 | A probability frequency distribution is a way to show how often an event will happen. It also shows what the probability of each event happening is. A frequency distribution table can be created by hand, or you can make a frequency distribution table in Excel. | |
C6179 | A t-test tests a null hypothesis about two means; most often, it tests the hypothesis that two means are equal, or that the difference between them is zero. A chi-square test tests a null hypothesis about the relationship between two variables. | |
C6180 | Neural style transfer is trained as a supervised learning task in which the goal is to input two images (x), and train a network to output a new, synthesized image (y). | |
C6181 | Negative binomial regression is for modeling count variables, usually for over-dispersed count outcome variables. Please note: The purpose of this page is to show how to use various data analysis commands. It does not cover all aspects of the research process which researchers are expected to do. | |
C6182 | A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. | |
C6183 | Data Mining Techniques: Algorithm, Methods & Top Data Mining#1) Frequent Pattern Mining/Association Analysis.#2) Correlation Analysis.#3) Classification.#4) Decision Tree Induction.#5) Bayes Classification.#6) Clustering Analysis.#7) Outlier Detection.#8) Sequential Patterns.More items• | |
C6184 | Similar to the t-test/correlation equivalence, the relationship between two dichotomous variables is the same as the difference between two groups when the dependent variable is dichotmous. The appropriate test to compare group differences with a dichotmous outcome is the chi-square statistic. | |
C6185 | In convolutional networks, multiple filters are taken to slice through the image and map them one by one and learn different portions of an input image. Imagine a small filter sliding left to right across the image from top to bottom and that moving filter is looking for, say, a dark edge. | |
C6186 | Weights and biases (commonly referred to as w and b) are the learnable parameters of a machine learning model. When the inputs are transmitted between neurons, the weights are applied to the inputs along with the bias. A neuron. Weights control the signal (or the strength of the connection) between two neurons. | |
C6187 | Sampling Frame vs. A sampling frame is a list of things that you draw a sample from. A sample space is a list of all possible outcomes for an experiment. For example, you might have a sampling frame of names of people in a certain town for a survey you're going to be conducting on family size. | |
C6188 | The Society for Imprecise Probability: Theories and Applications (SIPTA) was created in February 2002, with the aim of promoting the research on Imprecise probability. | |
C6189 | Clustering methods are used to identify groups of similar objects in a multivariate data sets collected from fields such as marketing, bio-medical and geo-spatial. They are different types of clustering methods, including: Partitioning methods. Hierarchical clustering. | |
C6190 | The gradient vector is scaled uniformly by a scalar learning rate . The learning rate remains constant throughout the learning process. | |
C6191 | TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations. | |
C6192 | How to Interpret ProbabilityIf P(A) equals zero, event A will almost definitely not occur.If P(A) is close to zero, there is only a small chance that event A will occur.If P(A) equals 0.5, there is a 50-50 chance that event A will occur.If P(A) is close to one, there is a strong chance that event A will occur.More items | |
C6193 | 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. Optimizers help to get results faster. | |
C6194 | Batch normalization enables the use of higher learning rates, greatly accelerating the learning process. It also enabled the training of deep neural networks with sigmoid activations that were previously deemed too difficult to train due to the vanishing gradient problem. | |
C6195 | Partitioning data into training, validation, and holdout sets allows you to develop highly accurate models that are relevant to data that you collect in the future, not just the data the model was trained on. | |
C6196 | The law of averages is the commonly held belief that a particular outcome or event will over certain periods of time occur at a frequency that is similar to its probability. Depending on context or application it can be considered a valid common-sense observation or a misunderstanding of probability. | |
C6197 | First, let me point out that there is nothing wrong with a positive log likelihood. The likelihood is the product of the density evaluated at the observations. Usually, the density takes values that are smaller than one, so its logarithm will be negative. Naturally, the logarithm of this value will be positive. | |
C6198 | AdaBoost is short for Adaptive Boosting and is a very popular boosting technique which combines multiple “weak classifiers” into a single “strong classifier”. It was formulated by Yoav Freund and Robert Schapire. They also won the 2003 Gödel Prize for their work. | |
C6199 | Disparate impact is often referred to as unintentional discrimination, whereas disparate treatment is intentional. Disparate treatment is intentional employment discrimination. For example, testing a particular skill of only certain minority applicants is disparate treatment. |
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