AI News, Advice for applying Machine Learning¶

Advice for applying Machine Learning¶

train_sizes=np.linspace(.1, 1.0, 5)):

Training vector, where n_samples is the number of samples and

If an integer is passed, it is the number of folds (defaults to 3).

sklearn.cross_validation module for the list of possible objects

estimator, X, y, cv=5, n_jobs=1, train_sizes=train_sizes)

train_scores_mean + train_scores_std, alpha=0.1,

color="r")

test_scores_mean + test_scores_std, alpha=0.1, color="g")

label="Training score")

label="Cross-validation score")

n_redundant=2, n_classes=2, random_state=0) from

columns = range(20) + ["class"]) Notice

This simple cheat-sheet (credit goes to Andreas Müller and the sklearn-team) can help to select an appropriate ML method for your problem (see http://dlib.net/ml_guide.svg for an alternative cheat sheet). In[8]:

width=800, height=600) Out[8]:Since we have 1000 samples, are predicting a category, and have labels, the sheet recommends that we use a LinearSVC (which stands for support vector classification with linear kernel and uses an efficient algorithm for solving this particular problem) first.

X, y, ylim=(0.8, 1.01),

train_sizes=np.linspace(.05, 0.2, 5)) We

X, y, ylim=(0.8, 1.1),

train_sizes=np.linspace(.1, 1.0, 5)) We

There are different ways of obtaining more data, for instance we (a) might invest the effort of collecting more, (b) create some artificially based on the existing ones (for images, e.g., rotation, translation, distortion), or (c) add artificial noise. If

X[:, [11, 14]], y, ylim=(0.8, 1.0),

train_sizes=np.linspace(.05, 0.2, 5)) Note

("svc", LinearSVC(C=10.0))]),

"SelectKBest(f_classif, k=2) + LinearSVC(C=10.0)",

X, y, ylim=(0.8, 1.0),

train_sizes=np.linspace(.05, 0.2, 5)) This

Others would be: (a) reduce the degree of a polynomial model in linear regression, (b) reduce the number of nodes/layers of an artificial neural network, (c) increase bandwidth of an RBF-kernel etc. One

X, y, ylim=(0.8, 1.0),

train_sizes=np.linspace(.05, 0.2, 5)) This

param_grid={"C": [0.001, 0.01, 0.1, 1.0, 10.0]}) plot_learning_curve(est,

X, y, ylim=(0.8, 1.0),

train_sizes=np.linspace(.05, 0.2, 5)) print

"LinearSVC(C=0.1, penalty='l1')",

X, y, ylim=(0.8, 1.0),

train_sizes=np.linspace(.05, 0.2, 5)) This

0.004135 0.

X, y, ylim=(0.5, 1.0),

train_sizes=np.linspace(.1, 1.0, 5)) Wow,

columns = range(2) + ["class"]) _

X_extra, y, ylim=(0.5, 1.0),

train_sizes=np.linspace(.1, 1.0, 5)) Perfectly!

"SVC(C=2.5, kernel='rbf', gamma=1.0)",

X, y, ylim=(0.5, 1.0),

train_sizes=np.linspace(.1, 1.0, 5)) Yes,

This classifier learns a linear model (just as LinearSVC or logistic regression) but uses stochastic gradient descent for training (just as artificial neural networks with backpropagation do typically).

Note that SGDClassifier is sensitive to feature scaling and thus a common preprocessing on real datasets is to standardize the data with, e.g., StandardScaler such that every feature has mean 0 and variance 1. SGDClassifier

instead progressive validation is used: here, the estimator is tested always on the next chunk of training data (before seeing it for training).

n_redundant=0, n_classes=10, class_sep=2,

random_state=0) In[23]:

plot tells us that after 50 mini-batches of data we are no longer improving on the validation data and could thus also stop training.

iy = 10 * j + 1

img[ix:ix + 8, iy:iy + 8] = X[i * n_img_per_row + j].reshape((8, 8)) plt.imshow(img,

have thus 1083 examples of hand-written digits (0, 1, 2, 3, 4, 5), where each of those consists of an $8 \times 8$ gray-scale image of 4-bit pixels (0, 16).

color=plt.cm.Set1(y[i] / 10.),

fontdict={'weight': 'bold', 'size': 12})

# only print thumbnails with matplotlib >

shown_images = np.array([[1., 1.]]) # just something big

dist = np.sum((X[i] - shown_images) ** 2, 1)

if np.min(dist) <

# don't show points that are too close

continue

shown_images = np.r_[shown_images, [X[i]]]

imagebox = offsetbox.AnnotationBbox(

offsetbox.OffsetImage(digits.images[i], cmap=plt.cm.gray_r),

X[i])

ax.add_artist(imagebox)

"Principal Components projection of the digits (time: %.3fs)"

"t-SNE embedding of the digits (time: %.3fs)"

hinge loss (used in support-vector classification) results in solutions which are sparse in the data (due to it being zero for $f(x) > 1$) and is relatively robust to outliers (it grows only linearly for $f(x)\to-\infty$) .

The perceptron loss, on the other hand, is happy as long as a datapoint is on the correct side of the boundary, which leaves the boundary under-determined if the data is truly linearly separable and results in worse generalization than a maximum-margin boundary. Summary¶ We

A first impression of a moderately complex signal processing pipeline can be obtained from a pySPACE example for detecting a specific event-related potential in EEG data: https://github.com/pyspace/pyspace/blob/master/docs/examples/specs/node_chains/ref_P300_flow.yaml This

signal processing pipeline contains nodes for data standardization, decimation, band-pass filtering, dimensionality reduction (xDAWN is a supervised method for this), feature extraction (Local_Straightline_Features), and feature normalization.

Image(filename='algorithm_types_detailed.png', width=800, height=600) Out[30]:One of the long-term goals of machine learning, which is pursued among others in the field of deep learning, is to allow to learn large parts of such pipelines rather than to hand-engineer them. In[31]:

Open Machine Learning Course. Topic 6. Feature Engineering and Feature Selection

In this course, we have already seen several key machine learning algorithms.

Any experienced professional can recall numerous times when a simple model trained on high-quality data was proven to be better than a complicated multi-model ensemble built on data that wasn’t clean.

To start, I wanted to review three similar but different tasks: This article will contain almost no math, but there will be a fair amount of code.

There are ready-to-use tokenizers that take into account peculiarities of the language, but they make mistakes as well, especially when you work with specific sources of text (newspapers, slang, misspellings, typos).

The easiest approach is called Bag of Words: we create a vector with the length of the dictionary, compute the number of occurrences of each word in the text, and place that number of occurrences in the appropriate position in the vector.

In practice, you need to consider stop words, the maximum length of the dictionary, more efficient data structures (usually text data is converted to a sparse vector), etc.

This approach is called TF-IDF (term frequency-inverse document frequency), which cannot be written in a few lines, so you should look into the details in references such as this wiki.

This is a classic example of operations that can be performed on vectorized concepts: king - man + woman = queen.

It is worth noting that this model does not comprehend the meaning of the words but simply tries to position the vectors such that words used in common context are close to each other.

the last fully connected layers of the network, adding new layers chosen for a specific task, and then training the network on new data.

If your task is to just vectorize the image (for example, to use some non-network classifier), you only need to remove the last layers and use the output from the previous layers: Here's a classifier trained on one dataset and adapted for a different one by "detaching"

Features generated by hand are still very useful: for example, for predicting the popularity of a rental listing, we can assume that bright apartments attract more attention and create a feature such as "the average value of the pixel".

For images, EXIF stores many useful meta-information: manufacturer and camera model, resolution, use of the flash, geographic coordinates of shooting, software used to process image and more.

Geographic data is not so often found in problems, but it is still useful to master the basic techniques for working with it, especially since there are quite a number of ready-to-use solutions in this field.

If you have a small amount of data, enough time, and no desire to extract fancy features, you can use reverse_geocoder in lieu of OpenStreetMap: When working with geoсoding, we must not forget that addresses may contain typos, which makes the data cleaning step necessary.

Coordinates contain fewer misprints, but its position can be incorrect due to GPS noise or bad accuracy in places like tunnels, downtown areas, etc.

Here, you can really unleash your imagination and invent features based on your life experience and domain knowledge: the proximity of a point to the subway, the number of stories in the building, the distance to the nearest store, the number of ATMs around, etc.

In that case, distances (great circle distance and road distance calculated by the routing graph), number of turns with the ratio of left to right turns, number of traffic lights, junctions, and bridges will be useful.

In general, when working with time series data, it is a good idea to have a calendar with public holidays, abnormal weather conditions, and other important events.

At the same time, if you encode them as categorical variables, you'll breed a large numbers of features and lose information about proximity -- the difference between 22 and 23 will be the same as the difference between 22 and 7.

This transformation preserves the distance between points, which is important for algorithms that estimate distance (kNN, SVM, k-means ...) However, the difference between such coding methods is down to the third decimal place in the metric.

Regarding time series — we will not go into too much detail here (mostly due to my personal lack of experience), but I will point you to a useful library that automatically generates features for time series.

By the way, the data from the IP-address is well combined with http_accept_language: if the user is sitting at the Chilean proxies and browser locale is ru_RU, something is unclean and worth a look in the corresponding column in the table (is_traveler_or_proxy_user).

simple example: suppose that the task is to predict the cost of an apartment from two variables — the distance from city center and the number of rooms.

But, to some extent, it protects against outliers: Another fairly popular option is MinMax Scaling, which brings all the points within a predetermined interval (typically (0, 1)).

If we assume that some data is not normally distributed but is described by the log-normal distribution, it can easily be transformed to a normal distribution: The lognormal distribution is suitable for describing salaries, price of securities, urban population, number of comments on articles on the internet, etc.

If there are a limited number of features, it is possible to generate all the possible interactions and then weed out the unnecessary ones using the techniques described in the next section.

Approaches to handling missing values are pretty straightforward: Easy-to-use library solutions sometimes suggest sticking to something like df = df.fillna(0) and not sweat the gaps.

But this is not the best solution: data preparation takes more time than building models, so thoughtless gap-filling may hide a bug in processing and damage the model.

As long as we work with toy datasets, the size of the data is not a problem, but, for real loaded production systems, hundreds of extra features will be quite tangible.

Two types of models are usually used: some “wooden” composition such as Random Forest or a linear model with Lasso regularization so that it is prone to nullify weights of weak features.

Train a model on a subset of features, store results, repeat for different subsets, and compare the quality of models to identify the best feature set.

Fix a small number N, iterate through all combinations of N features, choose the best combination, and then iterate through the combinations of (N + 1) features so that the previous best combination of features is fixed and only a single new feature is considered.

This algorithm can be reversed: start with the complete feature space and remove features one by one until it does not impair the quality of the model or until the desired number of features is reached.

Box 5046, 2600 GA Delft The Netherlands email: duin '@' ph.tn.tudelft.nl http : //www.ph.tn.tudelft.nl/~duin tel +31 15 2786143 Data Set Information: This dataset consists of features of handwritten numerals (`0'--`9') extracted from a collection of Dutch utility maps.

200 patterns per class (for a total of 2,000 patterns) have been digitized in binary images.

These digits are represented in terms of the following six feature sets (files): 1.

mfeat-pix: 240 pixel averages in 2 x 3 windows;

In each file the 2000 patterns are stored in ASCI on 2000 lines.

The first 200 patterns are of class `0', followed by sets of 200 patterns for each of the classes `1' - `9'.

Using the pixel-dataset (mfeat-pix) sampled versions of the original images may be obtained (15 x 16 pixels).

About Feature Scaling and Normalization

The result of standardization (or Z-score normalization) is that the features will be rescaled so that they’ll have the properties of a standard normal distribution with \mu = 0 and \sigma = 1 where \mu is the mean (average) and \sigma is the standard deviation from the mean;

standard scores (also called z scores) of the samples are calculated as follows: Standardizing the features so that they are centered around 0 with a standard deviation of 1 is not only important if we are comparing measurements that have different units, but it is also a general requirement for many machine learning algorithms.

with features being on different scales, certain weights may update faster than others since the feature values x_j play a role in the weight updates so that w_j := w_j + \Delta w_j, where

Without going into much depth regarding information gain and impurity measures, we can think of the decision as “is feature x_i >= some_val?” Intuitively, we can see that it really doesn’t matter on which scale this feature is (centimeters, Fahrenheit, a standardized scale – it really doesn’t matter).

For example, if we initialize the weights of a small multi-layer perceptron with tanh activation units to 0 or small random values centered around zero, we want to update the model weights “equally.” As a rule of thumb I’d say: When in doubt, just standardize the data, it shouldn’t hurt.

Another prominent example is the Principal Component Analysis, where we usually prefer standardization over Min-Max scaling, since we are interested in the components that maximize the variance (depending on the question and if the PCA computes the components via the correlation matrix instead of the covariance matrix;

In the following section, we will go through the following steps: In this step, we will randomly divide the wine dataset into a training dataset and a test dataset where the training dataset will contain 70% of the samples and the test dataset will contain 30%, respectively.

Let us think about whether it matters or not if the variables are centered for applications such as Principal Component Analysis (PCA) if the PCA is calculated from the covariance matrix (i.e., the k principal components are the eigenvectors of the covariance matrix that correspond to the k largest eigenvalues.

Let’s assume we have the 2 variables \bf{x} and \bf{y} Then the covariance between the attributes is calculated as Let us write the centered variables as The centered covariance would then be calculated as follows: But since after centering, \bar{x}' = 0 and \bar{y}' = 0 we have \sigma_{xy}' = \frac{1}{n-1} \sum_{i}^{n} x_i' y_i' which is our original covariance matrix if we resubstitute back the terms x'

Let c be the scaling factor for \bf{x} Given that the “original” covariance is calculated as the covariance after scaling would be calculated as: \sigma_{xy}' = \frac{1}{n-1} \sum_{i}^{n} (c \cdot x_i - c \cdot \bar{x})(y_i - \bar{y}) =

\sigma_{xy}' = c \cdot \sigma_{xy} Therefore, the covariance after scaling one attribute by the constant c will result in a rescaled covariance c \sigma_{xy} So if we’d scaled \bf{x} from pounds to kilograms, the covariance between \bf{x} and \bf{y} will be 0.453592 times smaller.

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