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C5000 | How to Choose a Machine Learning Model – Some GuidelinesCollect data.Check for anomalies, missing data and clean the data.Perform statistical analysis and initial visualization.Build models.Check the accuracy.Present the results. | |
C5001 | Covariance measures the directional relationship between the returns on two assets. A positive covariance means that asset returns move together while a negative covariance means they move inversely. | |
C5002 | Convergence almost surely implies convergence in probability, but not vice versa. That is, convergence to 0 in probability says that the 1's will get rarer and rarer as one looks ahead in the sequence. In contrast, almost surely is equivalent to the statement that, with probability 1, there exists such that for all . | |
C5003 | A factorial ANOVA compares means across two or more independent variables. Again, a one-way ANOVA has one independent variable that splits the sample into two or more groups, whereas the factorial ANOVA has two or more independent variables that split the sample in four or more groups. | |
C5004 | The main requirements that a clustering algorithm should satisfy are:scalability;dealing with different types of attributes;discovering clusters with arbitrary shape;minimal requirements for domain knowledge to determine input parameters;ability to deal with noise and outliers;More items | |
C5005 | Logarithmic scales reduce wide-ranging quantities to tiny scopes. For example, the decibel (dB) is a unit used to express ratio as logarithms, mostly for signal power and amplitude (of which sound pressure is a common example). In chemistry, pH is a logarithmic measure for the acidity of an aqueous solution. | |
C5006 | Non parametric tests are used when your data isn't normal. Therefore the key is to figure out if you have normally distributed data. For example, you could look at the distribution of your data. If your data is approximately normal, then you can use parametric statistical tests. | |
C5007 | FastText uses a simple and efficient baseline for sentence classification( represent sentences as bag of words (BoW) and train a linear classifier). It uses negative sampling , hierarchical softmax and N-gram features to reduce computational cost and improve efficiency. Have to say, all of the terms made my head spin. | |
C5008 | Discrete Random Variable. Has either a finite or countable number of values. The values of a discrete random variable can be plotted on a number line with space between each point. | |
C5009 | Any quantity that has both magnitude and direction is called a vector. The only difference is that tensor is the generalized form of scalars and vectors . Means scalars and vectors are the special cases of tensor quantities. Scalar is a tensor of rank 0 and vector is a tensor of rank 1. | |
C5010 | 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. The matrix operation being performed—convolution—is not traditional matrix multiplication, despite being similarly denoted by *. | |
C5011 | The answer to that is the Erlang distribution. The Gamma distribution is a generalization of that distribution using a continuous instead of a discrete parameter for the number of events. | |
C5012 | Train and serve a TensorFlow model with TensorFlow ServingTable of contents.Create your model. Import the Fashion MNIST dataset. Train and evaluate your model.Save your model.Examine your saved model.Serve your model with TensorFlow Serving. Add TensorFlow Serving distribution URI as a package source: Make a request to your model in TensorFlow Serving. Make REST requests. | |
C5013 | Overfitting can be identified by checking validation metrics such as accuracy and loss. The validation metrics usually increase until a point where they stagnate or start declining when the model is affected by overfitting. | |
C5014 | Class interval refers to the numerical width of any class in a particular distribution. It is defined as the difference between the upper-class limit and the lower class limit. Class Interval = Upper-Class limit – Lower class limit. | |
C5015 | The SD line goes through the point of averages, and has slope equal to SDY/SDX if the correlation coefficient r is greater than or equal to zero. The SD line has slope −SDY/SDX if r is negative. The line slopes up to the right, because r is positive (0.5 at first). | |
C5016 | When to use it Use Spearman rank correlation when you have two ranked variables, and you want to see whether the two variables covary; whether, as one variable increases, the other variable tends to increase or decrease. | |
C5017 | A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Both classes of networks exhibit temporal dynamic behavior. | |
C5018 | : the mean of the absolute values of the numerical differences between the numbers of a set (such as statistical data) and their mean or median. | |
C5019 | The exponential smoothing method takes this into account and allows for us to plan inventory more efficiently on a more relevant basis of recent data. Another benefit is that spikes in the data aren't quite as detrimental to the forecast as previous methods. | |
C5020 | To find the harmonic mean of a set of n numbers, add the reciprocals of the numbers in the set, divide the sum by n, then take the reciprocal of the result. | |
C5021 | Discriminant analysis is a versatile statistical method often used by market researchers to classify observations into two or more groups or categories. In other words, discriminant analysis is used to assign objects to one group among a number of known groups. | |
C5022 | Mixed effects models are useful when we have data with more than one source of random variability. For example, an outcome may be measured more than once on the same person (repeated measures taken over time). When we do that we have to account for both within-person and across-person variability. | |
C5023 | 1 : something within or from which something else originates, develops, or takes form an atmosphere of understanding and friendliness that is the matrix of peace. 2a : a mold from which a relief (see relief entry 1 sense 6) surface (such as a piece of type) is made. b : die sense 3a(1) | |
C5024 | Likelihood ratios (LR) in medical testing are used to interpret diagnostic tests. Basically, the LR tells you how likely a patient has a disease or condition. The higher the ratio, the more likely they have the disease or condition. Conversely, a low ratio means that they very likely do not. | |
C5025 | A final LSTM model is one that you use to make predictions on new data. That is, given new examples of input data, you want to use the model to predict the expected output. This may be a classification (assign a label) or a regression (a real value).28/08/2017 | |
C5026 | 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. | |
C5027 | The three axioms are:For any event A, P(A) ≥ 0. In English, that's “For any event A, the probability of A is greater or equal to 0”.When S is the sample space of an experiment; i.e., the set of all possible outcomes, P(S) = 1. If A and B are mutually exclusive outcomes, P(A ∪ B ) = P(A) + P(B). | |
C5028 | Item based collaborative filtering finds similarity patterns between items and recommends them to users based on the computed information, whilst user based finds similar users and gives them recommendations based on what other people with similar consumption patterns appreciated[3]. | |
C5029 | Bootstrapping is any test or metric that uses random sampling with replacement, and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates. It may also be used for constructing hypothesis tests. | |
C5030 | In Semantic networks, you can represent your knowledge in the form of graphical networks. This network consists of nodes representing objects and arcs which describe the relationship between those objects. Also, it categorizes the object in different forms and links those objects. | |
C5031 | Synset : a set of synonyms that share a common meaning. Each synset contains one or more lemmas, which represent a specific sense of a specific word. | |
C5032 | Validation set is used for tuning the parameters of a model. Test set is used for performance evaluation. 2. | |
C5033 | Probability sampling is based on the fact that every member of a population has a known and equal chance of being selected. With non-probability sampling, those odds are not equal. For example, a person might have a better chance of being chosen if they live close to the researcher or have access to a computer. | |
C5034 | The cumulative distribution function, CDF, or cumulant is a function derived from the probability density function for a continuous random variable. It gives the probability of finding the random variable at a value less than or equal to a given cutoff. | |
C5035 | A negative binomial distribution is concerned with the number of trials X that must occur until we have r successes. The number r is a whole number that we choose before we start performing our trials. The random variable X is still discrete. However, now the random variable can take on values of X = r, r+1, r+2, | |
C5036 | Latent Profile Analysis (LPA) tries to identify clusters of individuals (i.e., latent profiles) based on responses to a series of continuous variables (i.e., indicators). LPA assumes that there are unobserved latent profiles that generate patterns of responses on indicator items. | |
C5037 | However there are disadvantages to the use of second order derivatives. (We should note that first derivative operators exaggerate the effects of noise.) Second derivatives will exaggerated noise twice as much. No directional information about the edge is given. | |
C5038 | There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster. | |
C5039 | Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) | |
C5040 | Outlier detection is extensively used in a wide variety of applications such as military surveillance for enemy activities to prevent attacks, intrusion detection in cyber security, fraud detection for credit cards, insurance or health care and fault detection in safety critical systems and in various kind of images. | |
C5041 | General steps to calculate the mean squared error from a set of X and Y values:Find the regression line.Insert your X values into the linear regression equation to find the new Y values (Y').Subtract the new Y value from the original to get the error.Square the errors.Add up the errors.Find the mean. | |
C5042 | Spaced Practice. Space out your studying over time. Retrieval Practice. Practice bringing information to mind without the help of materials. Elaboration. Explain and describe ideas with many details. Interleaving. Switch between ideas while you study. Concrete Examples. Dual Coding. | |
C5043 | Artificial intelligence is probably the most widely-known for its application in the etail/retail industry. Conversation intelligence software helps companies interact with customers and follow up leads by analyzing and segmenting sales calls using speech recognition and natural language processing. | |
C5044 | In order to use MLE, we have to make two important assumptions, which are typically referred to together as the i.i.d. assumption. These assumptions state that: Data must be independently distributed. Data must be identically distributed. | |
C5045 | Object tracking is a discipline within computer vision, which aims to track objects as they move across a series of video frames. Objects are often people, but may also be animals, vehicles or other objects of interest, such as the ball in a game of soccer. | |
C5046 | AI has a high learning curve, but for motivated students, the rewards of an AI career far outweigh the investment of time and energy. Succeeding in the field usually requires a bachelor's degree in computer science or a related discipline such as mathematics. More senior positions may require a master's or Ph. | |
C5047 | The rank mean of one group is compared to the overall rank mean to determine a test statistic called a z-score. If the groups are evenly distributed, then the z-score will be closer to 0. | |
C5048 | Gradient Boosting Machines vs. XGBoost. While regular gradient boosting uses the loss function of our base model (e.g. decision tree) as a proxy for minimizing the error of the overall model, XGBoost uses the 2nd order derivative as an approximation. | |
C5049 | The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network. | |
C5050 | During training stage the residual network alters the weights until the output is equivalent to the identity function. In turn the identity function helps in building a deeper network. The residual function then maps the identity, weights and biases to fit the actual value. | |
C5051 | Amazon ML supports three types of ML models: binary classification, multiclass classification, and regression. The type of model you should choose depends on the type of target that you want to predict. | |
C5052 | In machine learning, the true positive rate, also referred to sensitivity or recall, is used to measure the percentage of actual positives which are correctly identified. | |
C5053 | Often, binary data is used to represent one of two conceptually opposed values, e.g: the outcome of an experiment ("success" or "failure") the response to a yes-no question ("yes" or "no") presence or absence of some feature ("is present" or "is not present") | |
C5054 | Accuracy in Machine Learning Accuracy is the number of correctly predicted data points out of all the data points. More formally, it is defined as the number of true positives and true negatives divided by the number of true positives, true negatives, false positives, and false negatives. | |
C5055 | AB testing is essentially an experiment where two or more variants of a page are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal. | |
C5056 | Categorical variables require special attention in regression analysis because, unlike dichotomous or continuous variables, they cannot by entered into the regression equation just as they are. Instead, they need to be recoded into a series of variables which can then be entered into the regression model. | |
C5057 | Vector autoregression (VAR) is a statistical model used to capture the relationship between multiple quantities as they change over time. VAR is a type of stochastic process model. VAR models generalize the single-variable (univariate) autoregressive model by allowing for multivariate time series. | |
C5058 | Distributions with one clear peak are called unimodal, and distributions with two clear peaks are called bimodal. When a symmetric distribution has a single peak at the center, it is referred to as bell-shaped. | |
C5059 | The Finite Population Correction Factor (FPC) is used when you sample without replacement from more than 5% of a finite population. It's needed because under these circumstances, the Central Limit Theorem doesn't hold and the standard error of the estimate (e.g. the mean or proportion) will be too big. | |
C5060 | In systematic sampling, the list of elements is "counted off". That is, every kth element is taken. Stratified sampling also divides the population into groups called strata. However, this time it is by some characteristic, not geographically. | |
C5061 | As we optimize the squared residuals to estimate the regression parameters, so we need commonly known normal situation. In Statistics, normal means that everything has equal probability. So equal probability for each value of regression residual is only possible through Normal Distribution. | |
C5062 | The network is symmetric because the weight wij for the connection between unit i and R. Rojas: Neural Networks, Springer-Verlag, Berlin, 1996 R. Rojas: Neural Networks, Springer-Verlag, Berlin, 1996 Page 8 344 13 The Hopfield Model unit j is equal to the weight wji of the connection from unit j to unit i. | |
C5063 | A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. | |
C5064 | The generalized delta rule is a mathematically derived formula used to determine how to update a neural network during a (back propagation) training step. A neural network learns a function that maps an input to an output based on given example pairs of inputs and outputs. | |
C5065 | 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. A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis. | |
C5066 | Discriminant analysis is statistical technique used to classify observations into non-overlapping groups, based on scores on one or more quantitative predictor variables. For example, a doctor could perform a discriminant analysis to identify patients at high or low risk for stroke. | |
C5067 | Reinforcement Learning (RL) refers to a kind of Machine Learning method in which the agent receives a delayed reward in the next time step to evaluate its previous action. It was mostly used in games (e.g. Atari, Mario), with performance on par with or even exceeding humans. | |
C5068 | Key Takeaways. Standard deviation defines the line along which a particular data point lies. Z-score indicates how much a given value differs from the standard deviation. The Z-score, or standard score, is the number of standard deviations a given data point lies above or below mean. | |
C5069 | Association Rule Mining, as the name suggests, association rules are simple If/Then statements that help discover relationships between seemingly independent relational databases or other data repositories. Most machine learning algorithms work with numeric datasets and hence tend to be mathematical. | |
C5070 | A sample space is the set of all possible outcomes. However, some sample spaces are better than others. Consider the experiment of flipping two coins. It is possible to get 0 heads, 1 head, or 2 heads. Thus, the sample space could be {0, 1, 2}. | |
C5071 | Simply stated, high risk prevention strategies aim to identify individuals or groups who are likely to have an increased incidence of a disease, based on the presence of modifiable risk factors known to be causal for the disease (e.g., high blood pressure), or characteristics of individuals or groups that are | |
C5072 | The cumulative distribution function FX(x) of a random variable X has three important properties: The cumulative distribution function FX(x) is a non-decreasing function. This follows directly from the result we have just derived: For a<b, we have Pr(a<X≤b)≥0 ⟹ FX(b)−FX(a)≥0 ⟹ FX(a)≤FX(b). | |
C5073 | Since this derivation of the LDA direction via least squares does not use a Gaussian assumption for the features, its applicability extends beyond the realm of Gaussian data. However the derivation of the particular intercept or cut-point given in (4.11) does require Gaussian data. | |
C5074 | 33:4235:21Suggested clip · 85 secondsDerivation of the Normal (Gaussian) Distribution - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5075 | In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In a census, data is sought for an entire population, hence the intended sample size is equal to the population. | |
C5076 | Naive Bayes Tutorial (in 5 easy steps)Step 1: Separate By Class.Step 2: Summarize Dataset.Step 3: Summarize Data By Class.Step 4: Gaussian Probability Density Function.Step 5: Class Probabilities. | |
C5077 | In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are derived via training. | |
C5078 | Gravity tries to keep things together through attraction and thus tends to lower statistical entropy. The universal law of increasing entropy (2nd law of thermodynamics) states that the entropy of an isolated system which is not in equilibrium will tend to increase with time, approaching a maximum value at equilibrium. | |
C5079 | The Generative Adversarial Network, or GAN, is an architecture that makes effective use of large, unlabeled datasets to train an image generator model via an image discriminator model. The discriminator model can be used as a starting point for developing a classifier model in some cases. | |
C5080 | To summarize, an algorithm is a method or a procedure we follow to get something done or solve a problem. A model is a computation or a formula formed as a result of an algorithm that takes some values as input and produces some value as output. | |
C5081 | A causal model is the most sophisticated kind of forecasting tool. It expresses mathematically the relevant causal relationships, and may include pipeline considerations (i.e., inventories) and market survey information. It may also directly incorporate the results of a time series analysis. | |
C5082 | 2 Answers. Boosting is based on weak learners (high bias, low variance). Boosting reduces error mainly by reducing bias (and also to some extent variance, by aggregating the output from many models). On the other hand, Random Forest uses as you said fully grown decision trees (low bias, high variance). | |
C5083 | How to Calculate a CorrelationFind the mean of all the x-values.Find the standard deviation of all the x-values (call it sx) and the standard deviation of all the y-values (call it sy). For each of the n pairs (x, y) in the data set, take.Add up the n results from Step 3.Divide the sum by sx ∗ sy.More items | |
C5084 | Artificial intelligence is generally divided into two types – narrow (or weak) AI and general AI, also known as AGI or strong AI. | |
C5085 | Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. The key idea is to set the target outcomes for this next model in order to minimize the error. | |
C5086 | 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. | |
C5087 | In computer science, a universal Turing machine (UTM) is a Turing machine that simulates an arbitrary Turing machine on arbitrary input. In terms of computational complexity, a multi-tape universal Turing machine need only be slower by logarithmic factor compared to the machines it simulates. | |
C5088 | TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf. data. Datasets , enabling easy-to-use and high-performance input pipelines. To get started see the guide and our list of datasets. | |
C5089 | The fundamental counting principle states that if there are p ways to do one thing, and q ways to do another thing, then there are p×q ways to do both things. possible outcomes of the experiment. The counting principle can be extended to situations where you have more than 2 choices. | |
C5090 | Three basic principles for the design of a sample survey are: 1. Principle of Optimization The principle of optimization takes into account the factors of (a) Efficiency and (b) cost. (a) Efficiency Efficiency is measured by the inverse of sampling variance of the estimator. | |
C5091 | Hierarchical regression is a way to show if variables of your interest explain a statistically significant amount of variance in your Dependent Variable (DV) after accounting for all other variables. This is a framework for model comparison rather than a statistical method. | |
C5092 | 1. A numerical value that defines the learning capability of a neural network during training. Learn more in: Voltage Instability Detection Using Neural Networks. | |
C5093 | The Poisson Model (distribution) Assumptions Independence: Events must be independent (e.g. the number of goals scored by a team should not make the number of goals scored by another team more or less likely.) Homogeneity: The mean number of goals scored is assumed to be the same for all teams. | |
C5094 | Essentially, cross-sectional analysis shows an investor which company is best given the metrics she cares about. Time series analysis, also known as trend analysis, focuses in on a single company over time. In this case, the company is being judged in the context of its past performance. | |
C5095 | Precision - Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. F1 score - F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. | |
C5096 | - if R-squared value 0.3 < r < 0.5 this value is generally considered a weak or low effect size, - if R-squared value 0.5 < r < 0.7 this value is generally considered a Moderate effect size, - if R-squared value r > 0.7 this value is generally considered strong effect size, Ref: Source: Moore, D. S., Notz, W. | |
C5097 | Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample. The clusters should ideally each be mini-representations of the population as a whole. | |
C5098 | One of the simplest causal analysis methods involves asking yourself “why” five times. You start by identifying the problem. “My house is always disorganized.” Then, you ask yourself why that is the case. You create a chain of inquiry that offers insight about the core of the problem. | |
C5099 | To find the interquartile range (IQR), first find the median (middle value) of the lower and upper half of the data. These values are quartile 1 (Q1) and quartile 3 (Q3). The IQR is the difference between Q3 and Q1. |
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