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C8300 | A sampling unit is a selection of a population that is used as an extrapolation of the population. For example, a household is used as a sampling unit, under the assumption that the polling results from this unit represents the opinions of a larger group. Related Courses. Guide to Audit Sampling. | |
C8301 | The key to interpreting a hierarchical cluster analysis is to look at the point at which any given pair of cards “join together” in the tree diagram. Cards that join together sooner are more similar to each other than those that join together later. | |
C8302 | Description. The alternating least squares (ALS) algorithm factorizes a given matrix R into two factors U and V such that R≈UTV. Since matrix factorization can be used in the context of recommendation, the matrices U and V can be called user and item matrix, respectively. | |
C8303 | One of the major disadvantages of the backpropagation learning rule is its ability to get stuck in local minima. The error is a function of all the weights in a multidimensional space. | |
C8304 | T-tests are called t-tests because the test results are all based on t-values. T-values are an example of what statisticians call test statistics. A test statistic is a standardized value that is calculated from sample data during a hypothesis test. | |
C8305 | This is in contrast to the Bernoulli, binomial, and hypergeometric distributions, where the number of possible values is finite. Whereas, in the geometric and negative binomial distributions, the number of "successes" is fixed, and we count the number of trials needed to obtain the desired number of "successes". | |
C8306 | by Tim Bock. Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other. | |
C8307 | Sampling helps a lot in research. It is one of the most important factors which determines the accuracy of your research/survey result. If anything goes wrong with your sample then it will be directly reflected in the final result. | |
C8308 | The F-distribution is a skewed distribution of probabilities similar to a chi-squared distribution. But where the chi-squared distribution deals with the degree of freedom with one set of variables, the F-distribution deals with multiple levels of events having different degrees of freedom. | |
C8309 | In multivariate regression there are more than one dependent variable with different variances (or distributions). The predictor variables may be more than one or multiple. But when we say multiple regression, we mean only one dependent variable with a single distribution or variance. | |
C8310 | Machine learning field allows you to code in a way so that the application or system can learn to solve the problem on it's own. Learning is a iterative process. | |
C8311 | Chunking in NLP is Changing a perception by moving a “chunk”, or a group of bits of information, in the direction of a Deductive or Inductive conclusion through the use of language. you will start to get smaller pieces of information about a car. | |
C8312 | The main problem of using adaptive learning rate optimizers including Adam, RMSProp, etc. is the difficulty of being stuck on local minima while not converging to the global minimum. These can lead to bad decisions of the optimizer and being stuck on local optima instead of finding global minima. | |
C8313 | ProcedureFrom the cluster management console, select Workload > Spark > Deep Learning.Select the Datasets tab.Click New.Create a dataset from Images for Object Classification.Provide a dataset name.Specify a Spark instance group.Specify image storage format, either LMDB for Caffe or TFRecords for TensorFlow.More items | |
C8314 | You can regularize your network by introducing a dropout layer soon after the convolution layer. So a typical layer of Conv->Relu becomes Conv->Dropout->Relu. You may play around with the architecture rather than simply use pre-defined ones like VGG or AlexNet. | |
C8315 | An example of a mutually exclusive event is when a coin is a tossed and there are two events that can occur, either it will be a head or a tail. Hence, both the events here are mutually exclusive.Difference between Mutually exclusive and independent eventsMutually exclusive eventsIndependent events4 more rows | |
C8316 | So year is a discretized measure of a continuous interval variable, so quantitative. | |
C8317 | Coefficient of correlation is “R” value which is given in the summary table in the Regression output. R square is also called coefficient of determination. Multiply R times R to get the R square value. In other words Coefficient of Determination is the square of Coefficeint of Correlation. | |
C8318 | The Binomial Theorem: Formulas. 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. | |
C8319 | In mathematics, statistics, finance, computer science, particularly in machine learning and inverse problems, regularization is the process of adding information in order to solve an ill-posed problem or to prevent overfitting. | |
C8320 | A symbol defining a class, such as 56 to 65 in Table (1), is called a class interval. The end numbers, 56 and 65, are called class limits; the smaller number (56) is the lower class limit, and the larger number (65) is the upper class limit. | |
C8321 | Advantages of Machine LearningContinuous Improvement. Machine Learning algorithms are capable of learning from the data we provide. Automation for everything. Trends and patterns identification. Wide range of applications. Data Acquisition. Highly error-prone. Algorithm Selection. Time-consuming. | |
C8322 | Then in July, Google launched AutoML for machine translation and natural language processing. These products have been adopted by Disney and Urban Outfitters in their practical applications. Behind AutoML is its engine called Neural Architecture Search, invented by Quoc Le, a pioneer in the AI Field. | |
C8323 | Examples of False Alarm Ratios The FAR would be: number of false alarms / the total number of warnings or alarms: 8/20 = 0.40. In weather reporting, the false alarm ratio for tornado warnings is the number of false tornado warnings per total number of tornado warnings. | |
C8324 | It's a method of evaluating how well specific algorithm models the given data. If predictions deviates too much from actual results, loss function would cough up a very large number. Gradually, with the help of some optimization function, loss function learns to reduce the error in prediction. | |
C8325 | A conjoint analysis step by step guide.Step 1: The Problem & Attribute. Step 2: The Preference Model. Step 3: The Data Collection. Step 4: Presentation of Alternatives. Step 5: The Experimental Design. Step 6: Measurement Scale. Step 7: Estimation Method. Conclusion. | |
C8326 | Definitions. The median (middle quartile) marks the mid-point of the data and is shown by the line that divides the box into two parts. Half the scores are greater than or equal to this value and half are less. The middle “box” represents the middle 50% of scores for the group. | |
C8327 | Categorical Imperative sees an action as right or wrong, based on moral duty, without taking the consequences into account. Rule utilitarianism views an action as right or wrong only when we take the consequences of the action into account. | |
C8328 | The points of our n dimensional space that obey a single one of our linear constraints as equalities, define a hyperplane. It is a plane-like region of n-1 dimensions in an n dimensional space. A hyperplane that actual forms part of the boundary of the feasible region is called an n-1 face of that region. | |
C8329 | "Mean" usually refers to the population mean. This is the mean of the entire population of a set. The mean of the sample group is called the sample mean. | |
C8330 | The Poisson parameter Lambda (λ) is the total number of events (k) divided by the number of units (n) in the data (λ = k/n). In between, or when events are infrequent, the Poisson distribution is used. | |
C8331 | The sampling distribution of the sample mean can be thought of as "For a sample of size n, the sample mean will behave according to this distribution." Any random draw from that sampling distribution would be interpreted as the mean of a sample of n observations from the original population. | |
C8332 | An example of Multiple stage sampling by clusters – An organization intends to survey to analyze the performance of smartphones across Germany. They can divide the entire country's population into cities (clusters) and select cities with the highest population and also filter those using mobile devices. | |
C8333 | In summary, model parameters are estimated from data automatically and model hyperparameters are set manually and are used in processes to help estimate model parameters. Model hyperparameters are often referred to as parameters because they are the parts of the machine learning that must be set manually and tuned. | |
C8334 | The finite population correction (fpc) factor is used to adjust a variance estimate for an estimated mean or total, so that this variance only applies to the portion of the population that is not in the sample. | |
C8335 | An odds ratio is a measure of association between the presence or absence of two properties. The value of the odds ratio tells you how much more likely someone under 25 might be to make a claim, for example, and the associated confidence interval indicates the degree of uncertainty associated with that ratio. | |
C8336 | 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 . | |
C8337 | Though unsupervised learning also could be used for anomaly detection, they are shown to perform very poorly compared to supervised or semi-supervised learning3. Reinforcement learning brings the full power of Artificial Intelligence to anomaly detection. | |
C8338 | To calculate how much weight you need, divide the known population percentage by the percent in the sample. For this example: Known population females (51) / Sample Females (41) = 51/41 = 1.24. Known population males (49) / Sample males (59) = 49/59 = . | |
C8339 | We obtain the moment generating function MX(t) from the expected value of the exponential function. We can then compute derivatives and obtain the moments about zero. M′X(t)=0.35et+0.5e2tM″X(t)=0.35et+e2tM(3)X(t)=0.35et+2e2tM(4)X(t)=0.35et+4e2t. | |
C8340 | The standard error is also inversely proportional to the sample size; the larger the sample size, the smaller the standard error because the statistic will approach the actual value. The standard error is considered part of descriptive statistics. It represents the standard deviation of the mean within a dataset. | |
C8341 | In exclusive form, the lower and upper limits are known as true lower limit and true upper limit of the class interval. Thus, class limits of 10 - 20 class intervals in the exclusive form are 10 and 20. In inclusive form, class limits are obtained by subtracting 0.5 from lower limitand adding 0.5 to the upper limit. | |
C8342 | : to aim an attack at someone or something. : to direct an action, message, etc., at someone or something. | |
C8343 | Convergence is the ability to turn the two eyes inward toward each other to look at a close object. We depend on this visual skill for near-work activities such as desk work at school, working on a smartphone type device, or even in sports when catching a ball. | |
C8344 | A true positive is an outcome where the model correctly predicts the positive class. Similarly, a true negative is an outcome where the model correctly predicts the negative class. A false positive is an outcome where the model incorrectly predicts the positive class. | |
C8345 | The difference between MLE/MAP and Bayesian inference MLE gives you the value which maximises the Likelihood P(D|θ). And MAP gives you the value which maximises the posterior probability P(θ|D). MLE and MAP returns a single fixed value, but Bayesian inference returns probability density (or mass) function. | |
C8346 | The value of the step size s depends on the fauntion. If it is too small the algorithm will be too slow. If it is too large the algrithm may over shoot the global minimum and behave eratically. Usually we set s to something like 0.01 and then adjust according to the results. | |
C8347 | Calculating the SVD consists of finding the eigenvalues and eigenvectors of AAT and ATA. The eigenvectors of ATA make up the columns of V , the eigenvectors of AAT make up the columns of U. Also, the singular values in S are square roots of eigenvalues from AAT or ATA. | |
C8348 | We can interpret the Poisson regression coefficient as follows: for a one unit change in the predictor variable, the difference in the logs of expected counts is expected to change by the respective regression coefficient, given the other predictor variables in the model are held constant. | |
C8349 | We discuss some wonders in the field of image processing with machine learning advancements. Image processing can be defined as the technical analysis of an image by using complex algorithms. Here, image is used as the input, where the useful information returns as the output. | |
C8350 | Principal Component Analysis (PCA) is used to explain the variance-covariance structure of a set of variables through linear combinations. It is often used as a dimensionality-reduction technique. | |
C8351 | SVMs and decision trees are discriminative models because they learn explicit boundaties between classes. SVM is a maximal margin classifier, meaning that it learns a decision boundary that maximizes the distance between samples of the two classes, given a kernel. | |
C8352 | Compressed sensing (also known as compressive sensing, compressive sampling, or sparse sampling) is a signal processing technique for efficiently acquiring and reconstructing a signal, by finding solutions to underdetermined linear systems. | |
C8353 | MGF Properties If two random variables have the same MGF, then they must have the same distribution. That is, if X and Y are random variables that both have MGF M(t), then X and Y are distributed the same way (same CDF, etc.). You could say that the MGF determines the distribution. | |
C8354 | A tensor is a generalization of vectors and matrices and is easily understood as a multidimensional array. It is a term and set of techniques known in machine learning in the training and operation of deep learning models can be described in terms of tensors. | |
C8355 | Data is the currency of applied machine learning. Therefore, it is important that it is both collected and used effectively. Data sampling refers to statistical methods for selecting observations from the domain with the objective of estimating a population parameter. | |
C8356 | Linear mixed models (sometimes called “multilevel models” or “hierarchical models”, depending on the context) are a type of regression model that take into account both (1) variation that is explained by the independent variables of interest (like lm() ) – fixed effects, and (2) variation that is not explained by the | |
C8357 | It's also important to understand what to focus on and what to do first.Pick a topic you are interested in. Find a quick solution. Improve your simple solution. Share your solution. Repeat steps 1-4 for different problems. Complete a Kaggle competition. Use machine learning professionally. | |
C8358 | The normal distribution can be used as an approximation to the binomial distribution, under certain circumstances, namely: If X ~ B(n, p) and if n is large and/or p is close to ½, then X is approximately N(np, npq) | |
C8359 | The Poisson distribution is a discrete function, meaning that the event can only be measured as occurring or not as occurring, meaning the variable can only be measured in whole numbers. Fractional occurrences of the event are not a part of the model. it was named after French mathematician Siméon Denis Poisson. | |
C8360 | The ReLu (Rectified Linear Unit) Layer ReLu refers to the Rectifier Unit, the most commonly deployed activation function for the outputs of the CNN neurons. Mathematically, it's described as: Unfortunately, the ReLu function is not differentiable at the origin, which makes it hard to use with backpropagation training. | |
C8361 | fits that relationship. That line is called a Regression Line and has the equation ŷ= a + b x. The Least Squares Regression Line is the line that makes the vertical distance from the data points to the regression line as small as possible. | |
C8362 | First, linear regression needs the relationship between the independent and dependent variables to be linear. It is also important to check for outliers since linear regression is sensitive to outlier effects. Multicollinearity occurs when the independent variables are too highly correlated with each other. | |
C8363 | Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance. | |
C8364 | A network access control list (ACL) is an optional layer of security for your VPC that acts as a firewall for controlling traffic in and out of one or more subnets. You might set up network ACLs with rules similar to your security groups in order to add an additional layer of security to your VPC. | |
C8365 | The level of statistical significance is often expressed as a p-value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. | |
C8366 | A GLM consists of three components: A random component, A systematic component, and. A link function. | |
C8367 | In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. | |
C8368 | In complete-linkage clustering, the link between two clusters contains all element pairs, and the distance between clusters equals the distance between those two elements (one in each cluster) that are farthest away from each other. | |
C8369 | x̄ = ( Σ xi ) / nAdd up the sample items.Divide sum by the number of samples.The result is the mean.Use the mean to find the variance.Use the variance to find the standard deviation. | |
C8370 | The resulting digital time record is then mathematically transformed into a frequency spectrum using an algorithm known as the Fast Fourier Transform, or FFT. The FFT is simply a clever set of operations which implements Fourier's theorem. The resulting spectrum shows the frequency components of the input signal. | |
C8371 | EXAMPLES OF DATA MINING APPLICATIONS Marketing. Data mining is used to explore increasingly large databases and to improve market segmentation. It is commonly applied to credit ratings and to intelligent anti-fraud systems to analyse transactions, card transactions, purchasing patterns and customer financial data. | |
C8372 | The t-value measures the size of the difference relative to the variation in your sample data. Put another way, T is simply the calculated difference represented in units of standard error. The greater the magnitude of T, the greater the evidence against the null hypothesis. | |
C8373 | Supervised: Use the target variable (e.g. remove irrelevant variables).Wrapper: Search for well-performing subsets of features. RFE.Filter: Select subsets of features based on their relationship with the target. Feature Importance Methods.Intrinsic: Algorithms that perform automatic feature selection during training. | |
C8374 | Hashing provides a more reliable and flexible method of data retrieval than any other data structure. It is faster than searching arrays and lists. In the same space it can retrieve in 1.5 probes anything stored in a tree that will otherwise take log n probes. | |
C8375 | In statistical hypothesis testing, the null distribution is the probability distribution of the test statistic when the null hypothesis is true. For example, in an F-test, the null distribution is an F-distribution. Null distribution is a tool scientists often use when conducting experiments. | |
C8376 | Cluster analysis is an exploratory analysis that tries to identify structures within the data. Cluster analysis is also called segmentation analysis or taxonomy analysis. More specifically, it tries to identify homogenous groups of cases if the grouping is not previously known. | |
C8377 | Momentum [1] or SGD with momentum is method which helps accelerate gradients vectors in the right directions, thus leading to faster converging. It is one of the most popular optimization algorithms and many state-of-the-art models are trained using it. | |
C8378 | Center: The center is not affected by sample size. The mean of the sample means is always approximately the same as the population mean µ = 3,500. Spread: The spread is smaller for larger samples, so the standard deviation of the sample means decreases as sample size increases. | |
C8379 | Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. | |
C8380 | Yes, you can use linear regression for prediction as long as the value of the unseen exploratory variable (x) is within the range of the x that was used to fit the linear model. | |
C8381 | Multivariate analysis is a set of statistical techniques used for analysis of data that contain more than one variable. Multivariate analysis refers to any statistical technique used to analyse more complex sets of data. | |
C8382 | 2. Why is it important to examine a residual plot even if a scatterplot appears to be linear? An examination of the of the residuals often leads us to discover groups of observations that are different from the rest. | |
C8383 | A decision tree is a flowchart-like diagram that shows the various outcomes from a series of decisions. It can be used as a decision-making tool, for research analysis, or for planning strategy. A primary advantage for using a decision tree is that it is easy to follow and understand. | |
C8384 | The following are examples of discrete probability distributions commonly used in statistics:Binomial distribution.Geometric Distribution.Hypergeometric distribution.Multinomial Distribution.Negative binomial distribution.Poisson distribution. | |
C8385 | Metaphor in Psychology Metaphors derive their power from how confused we are as human beings. Our brains have evolved to confuse the literal and the symbolic by cramming viscerally similar functions in the same brain areas. For example: The insula processes both physical and moral disgust. | |
C8386 | Definition. A sigmoid function is a bounded, differentiable, real function that is defined for all real input values and has a non-negative derivative at each point and exactly one inflection point. A sigmoid "function" and a sigmoid "curve" refer to the same object. | |
C8387 | Like I said before, the AUC-ROC curve is only for binary classification problems. But we can extend it to multiclass classification problems by using the One vs All technique. So, if we have three classes 0, 1, and 2, the ROC for class 0 will be generated as classifying 0 against not 0, i.e. 1 and 2. | |
C8388 | Unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. | |
C8389 | For trials with categorical outcomes (such as noting the presence or absence of a term), one way to estimate the probability of an event from data is simply to count the number of times an event occurred divided by the total number of trials. | |
C8390 | Parameters are key to machine learning algorithms. In this case, a parameter is a function argument that could have one of a range of values. In machine learning, the specific model you are using is the function and requires parameters in order to make a prediction on new data. | |
C8391 | There are various ways to modify a study design to actively exclude or control confounding variables (3) including Randomization, Restriction and Matching. In randomization the random assignment of study subjects to exposure categories to breaking any links between exposure and confounders. | |
C8392 | Enthalpy ( H ) is defined as the amount of energy released or absorbed during a chemical reaction. Entropy ( S ) defines the degree of randomness or disorder in a system. where at constant temperature, the change on free energy is defined as: ΔG=ΔH−TΔS . | |
C8393 | Unsupervised Learning is the second type of machine learning, in which unlabeled data are used to train the algorithm, which means it used against data that has no historical labels. | |
C8394 | The reason for using L1 norm to find a sparse solution is due to its special shape. It has spikes that happen to be at sparse points. Using it to touch the solution surface will very likely to find a touch point on a spike tip and thus a sparse solution. | |
C8395 | Parametric tests are those that make assumptions about the parameters of the population distribution from which the sample is drawn. This is often the assumption that the population data are normally distributed. Non-parametric tests are “distribution-free” and, as such, can be used for non-Normal variables. | |
C8396 | Normalization is the process of organizing data into a related table; it also eliminates redundancy and increases the integrity which improves performance of the query. To normalize a database, we divide the database into tables and establish relationships between the tables. | |
C8397 | AUC (Area Under Curve)-ROC (Receiver Operating Characteristic) is a performance metric, based on varying threshold values, for classification problems. As name suggests, ROC is a probability curve and AUC measure the separability. | |
C8398 | The Kolmogorov-Smirnov test (K-S) and Shapiro-Wilk (S-W) test are designed to test normality by comparing your data to a normal distribution with the same mean and standard deviation of your sample. If the test is NOT significant, then the data are normal, so any value above . 05 indicates normality. | |
C8399 | Correlation between a continuous and categorical variable There are three big-picture methods to understand if a continuous and categorical are significantly correlated — point biserial correlation, logistic regression, and Kruskal Wallis H Test. |
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