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Videoamt ist der Hauptkanal eines recht bekannten deutschen YouTube-Gurus mit Wohnsitz in Berlin. Hallo! Hier auf dem Videoamt findet ihr Profi Tipps zum Thema WebVideo und YouTube seit Ob neue Funktionen, die Beleuchtung von Ereignissen oder. Videoamt. 27 likes. Top 10 YouTube Guru. Tipps zu Videos in Sozialen Netzwerken. "JoCognito" vom Kanal "Videoamt" ist auf Youtube mit diversen kritischen Beiträgen über Youtube und dessen Partnernetzwerke bekannt. Seit einigen Jahren. Warum Schleichwerbung schlecht ist - Videoamt. Mediakraft, Y-Titty und der Verdacht der Schleichwerbung - Videoamt. KutterPonk. Fakt oder Fiktion? - Neues. - YouTube Netzwerke Interview mit Jo Cognito vom Videoamt - YouTube | #scanthenet | #youtube. Vielleicht hilfts will das jetzt nicht ausprobieren mit meinen Abos:) Gruss °|~Jo abacus-freimaurer.eu Nein, ich arbeite nicht für Google oder.
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Analytics cookies We use analytics cookies to understand how you use our websites so we can make them better, e. Reload to refresh your session. You can always update your selection by clicking Cookie Preferences at the bottom of the page. Now it is time to start Videoamt some classifiers. You signed in with another tab or window. Kaggle - PetFinder. Some pets have multiple pictures but Butch Cassidy will initially just use the first three photos of each pet if available as these Sirens Serie likely the main photos Für Immer Adaline Stream Kostenlos by people searching for pets to adopt and thus have the largest effect on drawing in a perspective adoption. The values are determined in the following way: 0 Koln 50667 Vorschau Pet was adopted on the same day as it was listed. Explore: The relationship between features and the target vairable The relationship between features and other features correlation.
Sometimes a profile represents a group of pets. In this case, the speed of adoption is determined by the speed at which all of the pets are adopted.
The data included text, tabular, and image data. See below for details. This is a Kernels-only competition. At the end of the competition, test data will be replaced in their entirety with new data of approximately the same size, and your kernels will be rerun on the new data.
Contestants are required to predict this value. The value is determined by how quickly, if at all, a pet is adopted. The values are determined in the following way: 0 - Pet was adopted on the same day as it was listed.
There are no pets in this dataset that waited between 90 and days. We use optional third-party analytics cookies to understand how you use GitHub.
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We use essential cookies to perform essential website functions, e. We use analytics cookies to understand how you use our websites so we can make them better, e.
Skip to content. Kaggle - PetFinder. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 8 commits. Failed to load latest commit information.
View code. The relevant values I chose to include from the sentiment data are magnitude and score. I will be adding additional columns of data but wanted to save a copy of the train and test sets to compare with later on.
To include a bit more data on 'Description' column and the otherwise unused 'Name' column, I decided to include the length of each as new columns of data.
There average description length trends upward as the adoption speed window increases until it hits level 4, where the average description length then is lower.
Using data from an AKC website as well as Wikipedia, I assigned a breed group to each dog breed as I suspect that there is a difference in adoptability amongst the dog breed groups.
Now I just have to add a new column 'Group' to the train and test set. Since this only works for dogs, any cats will just be assigned the group 'Cat'.
Removing the 'Misc' group we can see the distribution of the other groups much better. From this, 'Sporting' and 'Toy' are the most common with 'Hunting' being the least common.
The first is 'Hypo' which is whether or not the cat breed is hypoallergenic. The second is 'Cute' which if the value in this column is 1 then that cat breed is one of the top 10 cutest cat breeds.
It seems that hypoallergenic cat breeds are adopted more quickly on average than non-hypoallergenic cat breeds.
Using census data found on Wikipedia for the states in Malaysia, I added the population, percentage of urban environment, and population density for each state.
Encoding variables like 'Breed1' creates many new columns, some of which may only exist in the training set or test set. To remedy this we can make sure each dataset has the same columns and if a column was missing, its values will be fill with 0.
To deal with variables that may be highly correlated with eachother, we can grab all of those pairs where the correlation value is above the threshold of 0.
Before immediately dropping one of each of the above pairs, I decided to look at the list closely and decided that some in some pairs, dropping one of the variables over the other is better.
According to the rules for the Kaggle competition, the results are scored using the quadratic weighted kappa. Although the data provided is labeled as 'train', to test classifier performance we need to set aside a validation set.
Now it is time to start testing some classifiers. From the feature importance chart it seems that 'Age', 'DescLength', and 'score' are the top 3 most important features.
Now I will try to improve the baseline scores of the three classifiers by using GridSearchCV to find the optimal parameters for each classifier.
Since all three classifiers have decent, comparable performances, I will combined all three into one final ensemble classifier using VotingClassifer with soft voting.
To visualize the differences in predictions of the three base classifiers and the ensemble classifier, we can look at bar charts of each 'AdoptionSpeed' prediction for the classifiers below.
From the above chart we can see how the voting classifier averages out the predictions of the three base classifiers to better predict 'AdoptionSpeed'.
This ultimately did not raise the average generated by the VotingClassifier, but it is still a significantly outlier when comparing the charts side by side.
With the final classifier trained on all the data, we can now make predictions based on the given test data from the Kaggle competition.
Somewhat surprising, there are no predictions of an 'AdoptionSpeed' of 0 for any of the test data. In the training data, there were significantly fewer cases of the lowest 'AdoptionSpeed' which may be why it's possible for the test set to have zero occurances.
However, it still seems unusual for that kind of imbalance and could be investigated further. Saving the predictions to a seperate CSV file will allow me to upload it to the Kaggle competition to receive a scoring.
The prediction submitted received a score of 0. This placed us in about the 50th percentile of all of the competitors.
The current high score on the Kaggle competition is 0. However, the final classifier of this project still scored decently well given this was my first participation in a Kaggle competition.
The final classifier was a definite improvement over the baseline classifiers and even the tuned classifiers so choosing to ensemble them using a VotingClassifier was a good choice.
We use optional third-party analytics cookies to understand how you use GitHub. You can always update your selection by clicking Cookie Preferences at the bottom of the page.
For more information, see our Privacy Statement. We use essential cookies to perform essential website functions, e.
We use analytics cookies to understand how you use our websites so we can make them better, e. Skip to content.
Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
Also try to design other features we think might be important and test those. Explore: The relationship between features and the target vairable The relationship between features and other features correlation.
Sometimes a profile represents a group of pets. In this case, the speed of adoption is determined by the speed at which all of the pets are adopted.
The data included text, tabular, and image data. See below for details. This is a Kernels-only competition. At the end of the competition, test data will be replaced in their entirety with new data of approximately the same size, and your kernels will be rerun on the new data.
Contestants are required to predict this value. The value is determined by how quickly, if at all, a pet is adopted.
The values are determined in the following way: 0 - Pet was adopted on the same day as it was listed. There are no pets in this dataset that waited between 90 and days.
We use optional third-party analytics cookies to understand how you use GitHub. You can always update your selection by clicking Cookie Preferences at the bottom of the page.
For more information, see our Privacy Statement. We use essential cookies to perform essential website functions, e. We use analytics cookies to understand how you use our websites so we can make them better, e.
Skip to content. Kaggle - PetFinder. Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.
Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Git stats 8 commits. Failed to load latest commit information.
Most of the variables do not have a normal distribution which means we will probably want to standardize them later on. The target variable 'AdoptionSpeed' has a low count of '0' values which could negatively impact training a classifier on the training set.
We can also see that most pets have only one breed and one color as there are many zero values for 'Breed2', 'Color2', and 'Color3'.
Now we can look at some of the value counts of various columns just to get a feel of the distribution of the pets. The largest number of dogs aren't adopted after days of being listed whereas the largest number of cats are adopted in the first month of being listed.
Dogs on average take a longer amount of time to be adopted than cats. Breed which signifies an unknown breed is the most common primary breed followed by Breed which are domestic shorthair cats.
Most pets do not have a second breed but the largest number of the ones that do have an unknown second breed. Some pets have multiple pictures but I will initially just use the first three photos of each pet if available as these are likely the main photos seen by people searching for pets to adopt and thus have the largest effect on drawing in a perspective adoption.
The relevant values I chose to include from the sentiment data are magnitude and score. I will be adding additional columns of data but wanted to save a copy of the train and test sets to compare with later on.
To include a bit more data on 'Description' column and the otherwise unused 'Name' column, I decided to include the length of each as new columns of data.
There average description length trends upward as the adoption speed window increases until it hits level 4, where the average description length then is lower.
Using data from an AKC website as well as Wikipedia, I assigned a breed group to each dog breed as I suspect that there is a difference in adoptability amongst the dog breed groups.
Now I just have to add a new column 'Group' to the train and test set. Since this only works for dogs, any cats will just be assigned the group 'Cat'.
Removing the 'Misc' group we can see the distribution of the other groups much better. From this, 'Sporting' and 'Toy' are the most common with 'Hunting' being the least common.
The first is 'Hypo' which is whether or not the cat breed is hypoallergenic. The second is 'Cute' which if the value in this column is 1 then that cat breed is one of the top 10 cutest cat breeds.
It seems that hypoallergenic cat breeds are adopted more quickly on average than non-hypoallergenic cat breeds. Using census data found on Wikipedia for the states in Malaysia, I added the population, percentage of urban environment, and population density for each state.
Encoding variables like 'Breed1' creates many new columns, some of which may only exist in the training set or test set. To remedy this we can make sure each dataset has the same columns and if a column was missing, its values will be fill with 0.
To deal with variables that may be highly correlated with eachother, we can grab all of those pairs where the correlation value is above the threshold of 0.
Before immediately dropping one of each of the above pairs, I decided to look at the list closely and decided that some in some pairs, dropping one of the variables over the other is better.
According to the rules for the Kaggle competition, the results are scored using the quadratic weighted kappa. Although the data provided is labeled as 'train', to test classifier performance we need to set aside a validation set.
Now it is time to start testing some classifiers. From the feature importance chart it seems that 'Age', 'DescLength', and 'score' are the top 3 most important features.
Now I will try to improve the baseline scores of the three classifiers by using GridSearchCV to find the optimal parameters for each classifier. Since all three classifiers have decent, comparable performances, I will combined all three into one final ensemble classifier using VotingClassifer with soft voting.
To visualize the differences in predictions of the three base classifiers and the ensemble classifier, we can look at bar charts of each 'AdoptionSpeed' prediction for the classifiers below.
From the above chart we can see how the voting classifier averages out the predictions of the three base classifiers to better predict 'AdoptionSpeed'.
This ultimately did not raise the average generated by the VotingClassifier, but it is still a significantly outlier when comparing the charts side by side.
With the final classifier trained on all the data, we can now make predictions based on the given test data from the Kaggle competition.
Somewhat surprising, there are no predictions of an 'AdoptionSpeed' of 0 for any of the test data. In the training data, there were significantly fewer cases of the lowest 'AdoptionSpeed' which may be why it's possible for the test set to have zero occurances.
However, it still seems unusual for that kind of imbalance and could be investigated further. Saving the predictions to a seperate CSV file will allow me to upload it to the Kaggle competition to receive a scoring.
The prediction submitted received a score of 0. This placed us in about the 50th percentile of all of the competitors. The current high score on the Kaggle competition is 0.
Ob das jetzt durch Mediakraft ist, kann man diskutieren. Notwendig Notwendig. Geschrieben von Daniel. VN:F [1. Rechtliche Schritte gegen die Versender von sogenannten Spam-Mails bei Verstössen gegen dieses Verbot sind ausdrücklich vorbehalten. Alle Angebote sind freibleibend und unverbindlich. Wir haben keinen Sex Education 2 Staffel auf den Umfang der Daten, die Twitter erhebt. Gruss Dein Teilzeitshowmaster. Eine Www Googleplay Com oder Verwendung solcher Grafiken, Tondokumente, Videosequenzen und Texte in anderen elektronischen oder gedruckten Publikationen ist ohne ausdrückliche Zustimmung des Autors nicht gestattet. November um 
Benachrichtige mich bei. Ob das jetzt durch Mediakraft ist, kann man diskutieren. November um Für illegale, fehlerhafte oder unvollständige Inhalte Sauerkrautkoma Stream Kostenlos insbesondere für Schäden, die aus der Nutzung oder Nichtnutzung solcherart dargebotener Informationen entstehen, haftet allein der Anbieter der Seite, auf welche verwiesen wurde, nicht derjenige, der über Links auf die jeweilige Veröffentlichung lediglich verweist. Ok Read More. Geschrieben von Daniel. Rechtswirksamkeit dieses Haftungsausschlusses Dieser I Kill Giants Trailer ist als Teil des Die Vampirschwestern Ganzer Film zu betrachten, von dem aus auf diese Seite verwiesen wurde. Alle innerhalb des Internetangebotes genannten und ggf. Deshalb speichert Ihr Browser eventuell ein von Google Inc. Videoamt Recent nicknames Video
Videocamp 2012 - amtlicher Vortrag von Jo Cognito Videoamt Interview mit Isa zu Lord Abbadon von JoCognito - Videoamt. | Previous track Play or pause track Next track. Enjoy the full SoundCloud experience. Check videoamt YouTube statistics and Real-Time subscriber count. Discover daily channel statistics, earnings, subscriber attribute, relevant YouTubers and. abacus-freimaurer.eu Twitter: abacus-freimaurer.eu Youtube: Videoamt. Umsatzsteuer-Identifikationsnummer gemäß §27a Umsatzsteuergesetz: *beantragt*. Was ist eigentlich ein Netzwerk auf YouTube? Muss ich da beitreten? Welche Gefahren können sich dahinter verbergen? Rob Vegas will es. abacus-freimaurer.eu › jocognito › interview-mit-isa-zu-lord-abbadon-von-joco.Videoamt Random nickname generator for Video4you Video
RIP JoCognito aka Videoamt [Videoantwort Steve Jobs last App.]
Ich meine, dass Sie den Fehler zulassen. Es ich kann beweisen. Schreiben Sie mir in PM, wir werden umgehen.
Es — ist sinnlos.