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C7600 | Linear Discriminant Analysis or Normal Discriminant Analysis or Discriminant Function Analysis is a dimensionality reduction technique which is commonly used for the supervised classification problems. It is used for modeling differences in groups i.e. separating two or more classes. | |
C7601 | Logarithms are defined as the solutions to exponential equations and so are practically useful in any situation where one needs to solve such equations (such as finding how long it will take for a population to double or for a bank balance to reach a given value with compound interest). | |
C7602 | Characteristics of Normal Distribution Here, we see the four characteristics of a normal distribution. Normal distributions are symmetric, unimodal, and asymptotic, and the mean, median, and mode are all equal. | |
C7603 | A scatterplot displays the strength, direction, and form of the relationship between two quantitative variables. A correlation coefficient measures the strength of that relationship. The correlation r measures the strength of the linear relationship between two quantitative variables. | |
C7604 | KNN works by finding the distances between a query and all the examples in the data, selecting the specified number examples (K) closest to the query, then votes for the most frequent label (in the case of classification) or averages the labels (in the case of regression). | |
C7605 | Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that studies how machines understand human language. Its goal is to build systems that can make sense of text and perform tasks like translation, grammar checking, or topic classification. | |
C7606 | Values near −1 indicate a strong negative linear relationship, values near 0 indicate a weak linear relationship, and values near 1 indicate a strong positive linear relationship. The correlation is an appropriate numerical measure only for linear relationships and is sensitive to outliers. | |
C7607 | The sample variance will always be a smaller value than the population variance. The sample variance will always be a larger value than the population variance. | |
C7608 | The probability density function f(x), abbreviated pdf, if it exists, is the derivative of the cdf. Each random variable X is characterized by a distribution function FX(x). | |
C7609 | Extrapolation is a statistical method beamed at understanding the unknown data from the known data. It tries to predict future data based on historical data. For example, estimating the size of a population after a few years based on the current population size and its rate of growth. | |
C7610 | The degree of freedom is not a property of the distribution, it's the name of the distribution. It refers to the number of degrees of freedom of some variable that has the distribution. | |
C7611 | The range of ReLu is [0, inf). This means it can blow up the activation. Imagine a network with random initialized weights ( or normalised ) and almost 50% of the network yields 0 activation because of the characteristic of ReLu ( output 0 for negative values of x ). | |
C7612 | Unsupervised: Given only samples X of the data, we compute a function f such that y = f(X) is “simpler”. Clustering: y is discrete • Y is continuous: Matrix factorization, Kalman filtering, unsupervised neural networks. Unsupervised: Cluster some hand-written digit data into 10 classes. | |
C7613 | Word2Vec takes texts as training data for a neural network. The resulting embedding captures whether words appear in similar contexts. GloVe focuses on words co-occurrences over the whole corpus. Its embeddings relate to the probabilities that two words appear together. | |
C7614 | The measurable variable, as the name suggests, is the variable that is measured in an experiment. It is the dependent variable (DV), which depends on changes to the independent variable (IV). Any experiment studies the effects on the DV resulting from changes to the IV. | |
C7615 | A consistent learning algorithm is simply required to output a hypothesis that is consistent with all the training data provided to it. This notion of consistency is closely related to the empirical risk minimisation principle in the machine learning literature, where the risk is defined using the zero-one loss. | |
C7616 | We explore methods of producing adversarial examples on deep generative models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning architectures are known to be vulnerable to adversarial examples, but previous work has focused on the application of adversarial examples to classification tasks. | |
C7617 | Stochastic Gradient Descent (SGD): Hence, in Stochastic Gradient Descent, a few samples are selected randomly instead of the whole data set for each iteration. This problem is solved by Stochastic Gradient Descent. In SGD, it uses only a single sample, i.e., a batch size of one, to perform each iteration. | |
C7618 | The experiment results show that the accuracy of the model performance has a significant improvement by using hyperparameter optimization algorithms. Both Bayesian optimization and grid search perform almost equally well. However, Bayesian optimization runs faster than grid search. | |
C7619 | Training. AlphaGo Zero's neural network was trained using TensorFlow, with 64 GPU workers and 19 CPU parameter servers. In the first three days AlphaGo Zero played 4.9 million games against itself in quick succession. | |
C7620 | Rotation-invariant convolutional neural networks [9] rotate the input images in different angles, than compute different images with the same convolutional filters, the output feature maps of those are than concatenated together, Page 3 and one or more dense layers are stacked on top of it to achieve rotation | |
C7621 | For discrete data key distributions are: Bernoulli, Binomial, Poisson and Multinomial. | |
C7622 | 2. HIDDEN MARKOV MODELS. A hidden Markov model (HMM) is a statistical model that can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable. We call the observed event a `symbol' and the invisible factor underlying the observation a `state'. | |
C7623 | Linear regression attempts to model the relationship between two variables by fitting a linear equation (= a straight line) to the observed data. If you have a hunch that the data follows a straight line trend, linear regression can give you quick and reasonably accurate results. | |
C7624 | Huber loss is convex, differentiable, and also robust to outliers. | |
C7625 | The parameter lambda is called as the regularization parameter which denotes the degree of regularization. This is mainly because the weight W has a lot of parameters ( each neuron of each hidden layer ) while b has just one parameter which means the biases typically require less data than the weights to fit accurately. | |
C7626 | 0:007:21Suggested clip · 102 secondsBayesian posterior sampling - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7627 | Compressed sensing addresses the issue of high scan time by enabling faster acquisition by measuring fewer Fourier coefficients. This produces a high-quality image with relatively lower scan time. | |
C7628 | A disadvantage is when researchers can't classify every member of the population into a subgroup. Stratified random sampling is different from simple random sampling, which involves the random selection of data from the entire population so that each possible sample is equally likely to occur. | |
C7629 | : a principle of choice for a decision problem: one should choose the action which minimizes the loss that can be suffered even under the worst circumstances. | |
C7630 | Gini index < 0.2 represents perfect income equality, 0.2–0.3 relative equality, 0.3–0.4 adequate equality, 0.4–0.5 big income gap, and above 0.5 represents severe income gap. | |
C7631 | To calculate the total variance, you would subtract the average actual value from each of the actual values, square the results and sum them. From there, divide the first sum of errors (explained variance) by the second sum (total variance), subtract the result from one, and you have the R-squared. | |
C7632 | 0:0411:21Suggested clip · 104 secondsThe Binomial Theorem - Example 1 - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7633 | Gradient Descent is an optimization algorithm for finding a local minimum of a differentiable function. Gradient descent is simply used to find the values of a function's parameters (coefficients) that minimize a cost function as far as possible. | |
C7634 | Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen, such as death in biological organisms and failure in mechanical systems. Even in biological problems, some events (for example, heart attack or other organ failure) may have the same ambiguity. | |
C7635 | The normal distribution, commonly known as the bell curve, occurs throughout statistics. It is actually imprecise to say "the" bell curve in this case, as there are an infinite number of these types of curves. Above is a formula that can be used to express any bell curve as a function of x. | |
C7636 | When a sampling unit is drawn from a finite population and is returned to that population, after its characteristic(s) have been recorded, before the next unit is drawn, the sampling is said to be “with replacement”. | |
C7637 | Feature extraction is process of computing preselected features of EMG signals to be fed to a processing scheme (such as classifier) to improve the performance of the EMG based control system. | |
C7638 | Nodes are then organized into layers to comprise a network. A single-layer artificial neural network, also called a single-layer, has a single layer of nodes, as its name suggests. Each node in the single layer connects directly to an input variable and contributes to an output variable. | |
C7639 | A machine-learning algorithm that involves a Gaussian process uses lazy learning and a measure of the similarity between points (the kernel function) to predict the value for an unseen point from training data. | |
C7640 | The main types of probability sampling methods are simple random sampling, stratified sampling, cluster sampling, multistage sampling, and systematic random sampling. | |
C7641 | The Pearson's correlation coefficient is calculated as the covariance of the two variables divided by the product of the standard deviation of each data sample. It is the normalization of the covariance between the two variables to give an interpretable score. | |
C7642 | An affine layer, or fully connected layer, is a layer of an artificial neural network in which all contained nodes connect to all nodes of the subsequent layer. Affine layers are commonly used in both convolutional neural networks and recurrent neural networks. | |
C7643 | Random Forest is one of the most popular and most powerful machine learning algorithms. It is a type of ensemble machine learning algorithm called Bootstrap Aggregation or bagging. | |
C7644 | Cohen's kappa. Cohen suggested the Kappa result be interpreted as follows: values ≤ 0 as indicating no agreement and 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement. | |
C7645 | 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. | |
C7646 | In this way the keypoint is "orientation invariant" in the sense that if the same keypoint were found in a rotated image, the dominant orientation alignment/subtraction would guarantee the same (or similar) set of orientation histograms and therefore, keypoint signature. | |
C7647 | Sensitivity or the true positive rate is the probability that a test will result positive (indicate disease) amongst the subject with the disease. This is also a measure of the avoidance of false negatives.Variables and formulas.ConceptFormulaFalse Negative Rate100 x False Negative / (True Positive + False Negative)3 more rows• | |
C7648 | And the Machine Learning – The Naïve Bayes Classifier. It is a classification technique based on Bayes' theorem with an assumption of independence between predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. | |
C7649 | Dropout is a technique used to prevent a model from overfitting. Dropout works by randomly setting the outgoing edges of hidden units (neurons that make up hidden layers) to 0 at each update of the training phase. | |
C7650 | Tokenization breaks the raw text into words, sentences called tokens. These tokens help in understanding the context or developing the model for the NLP. The tokenization helps in interpreting the meaning of the text by analyzing the sequence of the words. Tokenization can be done to either separate words or sentences. | |
C7651 | The value of the z-score tells you how many standard deviations you are away from the mean. If a z-score is equal to 0, it is on the mean. A positive z-score indicates the raw score is higher than the mean average. For example, if a z-score is equal to +1, it is 1 standard deviation above the mean. | |
C7652 | When we decompose a complex problem we often find patterns among the smaller problems we create. Pattern recognition is one of the four cornerstones of Computer Science. It involves finding the similarities or patterns among small, decomposed problems that can help us solve more complex problems more efficiently. | |
C7653 | A random forest is simply a collection of decision trees whose results are aggregated into one final result. Their ability to limit overfitting without substantially increasing error due to bias is why they are such powerful models. One way Random Forests reduce variance is by training on different samples of the data. | |
C7654 | Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). Clustering can therefore be formulated as a multi-objective optimization problem. | |
C7655 | A nerve is essentially a collection of axon bundles found in the peripheral nervous system. The axons are wrapped in three layers connective tissue for protection and insulation. A neuron, on the other hand, has only one axon, it may be branched and extend in more than one direction. | |
C7656 | Log loss is used when we have {0,1} response. This is usually because when we have {0,1} response, the best models give us values in terms of probabilities. In simple words, log loss measures the UNCERTAINTY of the probabilities of your model by comparing them to the true labels. | |
C7657 | Qualitative Variables - Variables that are not measurement variables. Their values do not result from measuring or counting. Examples: hair color, religion, political party, profession. Designator - Values that are used to identify individuals in a table. | |
C7658 | S-Curves are used to visualize the progress of a project over time. They plot either cumulative work, based on person-hours, or costs over time. The name is derived from the fact that the data usually takes on an S-shape, with slower progress at the beginning and end of a project. | |
C7659 | This is because a two-tailed test uses both the positive and negative tails of the distribution. In other words, it tests for the possibility of positive or negative differences. A one-tailed test is appropriate if you only want to determine if there is a difference between groups in a specific direction. | |
C7660 | There are four assumptions associated with a linear regression model: Linearity: The relationship between X and the mean of Y is linear. Homoscedasticity: The variance of residual is the same for any value of X. Independence: Observations are independent of each other. | |
C7661 | The process of training an ML model involves providing an ML algorithm (that is, the learning algorithm) with training data to learn from. The term ML model refers to the model artifact that is created by the training process. You can use the ML model to get predictions on new data for which you do not know the target. | |
C7662 | Systematic Sampling Versus Cluster Sampling Cluster sampling breaks the population down into clusters, while systematic sampling uses fixed intervals from the larger population to create the sample. Cluster sampling divides the population into clusters and then takes a simple random sample from each cluster. | |
C7663 | Mean rank. The mean rank is the average of the ranks for all observations within each sample. Minitab uses the mean rank to calculate the H-value, which is the test statistic for the Kruskal-Wallis test. If two or more observations are tied, Minitab assigns the average rank to each tied observation. | |
C7664 | To improve CNN model performance, we can tune parameters like epochs, learning rate etc..Train with more data: Train with more data helps to increase accuracy of mode. Large training data may avoid the overfitting problem. Early stopping: System is getting trained with number of iterations. Cross validation: | |
C7665 | When training data is split into small batches, each batch is jargoned as a minibatch. If one updates model parameters after processing the whole training data (i.e., epoch), it would take too long to get a model update in training, and the entire training data probably won't fit in the memory. | |
C7666 | This axiom is controversial because although it seems like a relatively intuitive idea, there are still some issues. Also, the axiom of choice is equivalent to the statement that any set can be well-ordered, i.e., every nonempty set can be endowed with a total order such that every nonempty subset has a least element. | |
C7667 | 13. What is the difference between unimodal, bimodal, and multimodal data? Unimodal data has a distribution that is single-peaked (one mode). Bimodal data has two peaks (2 modes) and multimodal data refer to distributions with more than two clear peaks. | |
C7668 | Lasso regression is a type of linear regression that uses shrinkage. Shrinkage is where data values are shrunk towards a central point, like the mean. The lasso procedure encourages simple, sparse models (i.e. models with fewer parameters). | |
C7669 | With inferential statistics, you take data from samples and make generalizations about a population. This means taking a statistic from your sample data (for example the sample mean) and using it to say something about a population parameter (i.e. the population mean). Hypothesis tests. | |
C7670 | Correlation coefficients are indicators of the strength of the relationship between two different variables. A correlation coefficient that is greater than zero indicates a positive relationship between two variables. A value that is less than zero signifies a negative relationship between two variables. | |
C7671 | Tokenization breaks the raw text into words, sentences called tokens. These tokens help in understanding the context or developing the model for the NLP. The tokenization helps in interpreting the meaning of the text by analyzing the sequence of the words. | |
C7672 | The objective is to reduce the error e, which is the difference between the neuron response a, and the target vector t. The perceptron learning rule learnp calculates desired changes to the perceptron's weights and biases given an input vector p, and the associated error e. | |
C7673 | In a normal distribution, the mean and the median are the same number while the mean and median in a skewed distribution become different numbers: A left-skewed, negative distribution will have the mean to the left of the median. A right-skewed distribution will have the mean to the right of the median. | |
C7674 | A scatterplot displays the strength, direction, and form of the relationship between two quantitative variables. A correlation coefficient measures the strength of that relationship. The correlation r measures the strength of the linear relationship between two quantitative variables. | |
C7675 | A coefficient of correlation of +0.8 or -0.8 indicates a strong correlation between the independent variable and the dependent variable. An r of +0.20 or -0.20 indicates a weak correlation between the variables. | |
C7676 | The difference between interval and ratio scales comes from their ability to dip below zero. Interval scales hold no true zero and can represent values below zero. For example, you can measure temperature below 0 degrees Celsius, such as -10 degrees. Ratio variables, on the other hand, never fall below zero. | |
C7677 | A qualitative variable is a variable that expresses a quality. Values do not have numerical meaning and cannot be ordered numerically. Height, mass, age, and shoe size would all be measured in terms of numbers. So, these categories do not contain qualitative data. | |
C7678 | 2. HIDDEN MARKOV MODELS. A hidden Markov model (HMM) is a statistical model that can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable. We call the observed event a `symbol' and the invisible factor underlying the observation a `state'. | |
C7679 | The linear regression coefficients b 1 and b 3 describe the autoregressive effects, or the effect of a construct on itself measured at a later time. The autoregressive effects describe the stability of the constructs from one occasion to the next. | |
C7680 | Variables that can only take on a finite number of values are called "discrete variables." All qualitative variables are discrete. Some quantitative variables are discrete, such as performance rated as 1,2,3,4, or 5, or temperature rounded to the nearest degree. | |
C7681 | Measures of dispersion include: variance, standard deviation, and interquartile range. 3. 50th percentile states the value its not a measure of despersion. | |
C7682 | In essence, what the kernel trick does for us is to offer a more efficient and less expensive way to transform data into higher dimensions. With that saying, the application of the kernel trick is not limited to the SVM algorithm. | |
C7683 | We use binary cross-entropy loss for classification models which output a probability p. The range of the sigmoid function is [0, 1] which makes it suitable for calculating probability. | |
C7684 | An operating system (OS) is a set of functions or programs that coordinate a user program's access to the computer's resources (i.e. memory and CPU). These functions are called the MicroStamp11's kernel functions. | |
C7685 | Pros: It is easy and fast to predict class of test data set. It also perform well in multi class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data. | |
C7686 | A search algorithm is applied to a state space representation to find a solution path. Each search algorithm applies a particular search strategy. If states in the solution space can be revisited more than once a directed graph is used to represent the solution space. | |
C7687 | In their first layers, convolutional neural nets have 'filters'. Then, the filter is slid (or convolved), so it is now multiplied by a different section of the input, but the filter still has the same weights. Hence the shared weights. | |
C7688 | A normal distribution is symmetrical and bell-shaped. The Empirical Rule is a statement about normal distributions. The 95% Rule states that approximately 95% of observations fall within two standard deviations of the mean on a normal distribution. | |
C7689 | A disadvantage is when researchers can't classify every member of the population into a subgroup. Stratified random sampling is different from simple random sampling, which involves the random selection of data from the entire population so that each possible sample is equally likely to occur. | |
C7690 | Essentially, the process goes as follows:Select k centroids. These will be the center point for each segment.Assign data points to nearest centroid.Reassign centroid value to be the calculated mean value for each cluster.Reassign data points to nearest centroid.Repeat until data points stay in the same cluster. | |
C7691 | Machine learning models require all input and output variables to be numeric. This means that if your data contains categorical data, you must encode it to numbers before you can fit and evaluate a model. The two most popular techniques are an Ordinal Encoding and a One-Hot Encoding. | |
C7692 | Values between 0.7 and 1.0 (−0.7 and −1.0) indicate a strong positive (negative) linear relationship through a firm linear rule. It is the correlation coefficient between the observed and modelled (predicted) data values. It can increase as the number of predictor variables in the model increases; it does not decrease. | |
C7693 | The geometric distribution would represent the number of people who you had to poll before you found someone who voted independent. You would need to get a certain number of failures before you got your first success. If you had to ask 3 people, then X=3; if you had to ask 4 people, then X=4 and so on. | |
C7694 | Shannon entropy is never negative since it is minus the logarithm of a probability between zero and one. Minus a minus yields a positive for Shannon entropy. Like thermodynamic entropy, Shannon's information entropy is an index of disorder—unexpected or surprising bits. | |
C7695 | Insufficient training data is another cause of algorithmic bias. If the data used to train the algorithm are more representative of some groups of people than others, the predictions from the model may also be systematically worse for unrepresented or under-representative groups. | |
C7696 | four ways | |
C7697 | Abstract. Survival analysis, or more generally, time-to-event analysis, refers to a set of methods for analyzing the length of time until the occurrence of a well-defined end point of interest. The occurrence of a well-defined event such as patient mortality is often a primary outcome in medical research. | |
C7698 | Unlikely to CNN, RNN learns to recognize image features across time. Although RNN can be used for image classification theoretically, only a few researches about RNN image classifier can be found. | |
C7699 | An example of cluster sampling is area sampling or geographical cluster sampling. Each cluster is a geographical area. Because a geographically dispersed population can be expensive to survey, greater economy than simple random sampling can be achieved by grouping several respondents within a local area into a cluster. |
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